diff --git a/Documentation/ProjectFile/prj/processes/process/ComponentTransport/t_is_linear.md b/Documentation/ProjectFile/prj/processes/process/ComponentTransport/t_is_linear.md
index 521797f6c6e41c0f93363097a10a44092bcdd6eb..76ed6c9fb5255ae5a210f09a35b8d4289842ecb9 100644
--- a/Documentation/ProjectFile/prj/processes/process/ComponentTransport/t_is_linear.md
+++ b/Documentation/ProjectFile/prj/processes/process/ComponentTransport/t_is_linear.md
@@ -1,10 +1,12 @@
-This flag will enable two optimizations for the ComponentTransport process:
+This flag enables two optimizations for the ComponentTransport process:
 
 1. the global equation systems are assembled only once, in the first timestep,
    and reused subsequently
 2. the non-linear solver will converge in a single iteration in each timestep
 
 \attention
-With this option enabled, OGS will produce correct results only if the process equations are linear.
+With this option enabled, **OGS will produce correct results only if the process equations are linear**.
 OGS will not detect any non-linearities. It is the responsibility of the user to
 ensure that the assembled equation systems are linear, indeed!
+Furthermore, the material properties and parameters used in the process's
+equations must **not carry any time dependence**.
diff --git a/Documentation/ProjectFile/prj/processes/process/ComponentTransport/t_linear_solver_compute_only_upon_timestep_change.md b/Documentation/ProjectFile/prj/processes/process/ComponentTransport/t_linear_solver_compute_only_upon_timestep_change.md
new file mode 100644
index 0000000000000000000000000000000000000000..dc4e00b0f4a35cbe6c12d7ff902624a0e56da5e1
--- /dev/null
+++ b/Documentation/ProjectFile/prj/processes/process/ComponentTransport/t_linear_solver_compute_only_upon_timestep_change.md
@@ -0,0 +1,37 @@
+This flag enables an optimization for the ComponentTransport process:
+
+the linear solver will only do the `compute()`<sup>\*</sup> step if the timestep size
+changes.
+
+This flag is a further optimization on top of the
+[\<is_linear\>](@ref ogs_file_param__prj__processes__process__ComponentTransport__is_linear)
+flag.
+So the requirements of `<is_linear>` apply to this flag, too!
+
+\attention
+This is an expert option. It comes with a number of further **requirements above
+those of `<is_linear>`**. These are:
+
+- The linear solver used to solve the process equations must be exclusively used
+  for a single process (or for a single `process_id` in the case of staggered
+  coupling). Otherwise the equation system the linear solver has to solve would
+  not only change upon timestep size change, but also when switching processes.
+- Both, the process equations and the contributions from source terms and
+  boundary conditions must be **solution and time independent**!
+  In particular, the matrices presented to the linear solver must only change
+  upon time step size change.
+  Note that some boundary conditions (Robin, Python, ...) might contribute not only
+  to the right-hand side of the equation system, but also to the matrix.
+  The contributions to the matrix must be solution and time independent!
+  Also, switching Dirichlet BCs on and off for certain d.o.f.s (e.g., via Python
+  boundary conditions) is not possible!
+
+\attention
+OGS does not detect whether any or all of the above conditions are met.
+It is the sole responsibility of the user to ensure the correct usage of this
+expert option!
+The user is **strongly advised to check the correct use of this option** by
+comparing results to simulation runs that do not use this option!
+
+<sup>\*</sup>: `compute()` computes the LU decomposition for a direct linear
+solver, or computes the preconditioner for an iterative linear solver.
diff --git a/MathLib/LinAlg/Eigen/EigenLinearSolver.cpp b/MathLib/LinAlg/Eigen/EigenLinearSolver.cpp
index 21af0e4a701f365e15158680132951c5a74b3a29..54c5f65a6dfc43a2fabedef1cbbf19fc0aefd05d 100644
--- a/MathLib/LinAlg/Eigen/EigenLinearSolver.cpp
+++ b/MathLib/LinAlg/Eigen/EigenLinearSolver.cpp
@@ -12,6 +12,7 @@
 
 #include <Eigen/Sparse>
 
+#include "BaseLib/Error.h"
 #include "BaseLib/Logging.h"
 
 #ifdef USE_MKL
@@ -27,7 +28,6 @@
 #endif
 
 #include "EigenMatrix.h"
-#include "EigenTools.h"
 #include "EigenVector.h"
 
 namespace MathLib
@@ -41,9 +41,49 @@ public:
 
     virtual ~EigenLinearSolverBase() = default;
 
-    //! Solves the linear equation system \f$ A x = b \f$ for \f$ x \f$.
-    virtual bool solve(Matrix& A, Vector const& b, Vector& x,
-                       EigenOption& opt) = 0;
+    bool solve(Vector& b, Vector& x, EigenOption& opt)
+    {
+#ifdef USE_EIGEN_UNSUPPORTED
+        if (scaling_)
+        {
+            b = scaling_->LeftScaling().cwiseProduct(b);
+        }
+#endif
+
+        auto const success = solveImpl(b, x, opt);
+
+        if (scaling_)
+        {
+            x = scaling_->RightScaling().cwiseProduct(x);
+        }
+
+        return success;
+    }
+
+    bool compute(Matrix& A, EigenOption& opt)
+    {
+#ifdef USE_EIGEN_UNSUPPORTED
+        if (opt.scaling)
+        {
+            INFO("-> scale");
+            scaling_ = std::make_unique<
+                Eigen::IterScaling<EigenMatrix::RawMatrixType>>();
+            scaling_->computeRef(A);
+        }
+#endif
+
+        return computeImpl(A, opt);
+    }
+
+protected:
+    virtual bool solveImpl(Vector const& b, Vector& x, EigenOption& opt) = 0;
+
+    virtual bool computeImpl(Matrix& A, EigenOption& opt) = 0;
+
+private:
+#ifdef USE_EIGEN_UNSUPPORTED
+    std::unique_ptr<Eigen::IterScaling<EigenMatrix::RawMatrixType>> scaling_;
+#endif
 };
 
 namespace details
@@ -53,10 +93,25 @@ template <class T_SOLVER>
 class EigenDirectLinearSolver final : public EigenLinearSolverBase
 {
 public:
-    bool solve(Matrix& A, Vector const& b, Vector& x, EigenOption& opt) override
+    bool solveImpl(Vector const& b, Vector& x, EigenOption& opt) override
     {
         INFO("-> solve with Eigen direct linear solver {:s}",
              EigenOption::getSolverName(opt.solver_type));
+
+        x = solver_.solve(b);
+        if (solver_.info() != Eigen::Success)
+        {
+            ERR("Failed during Eigen linear solve");
+            return false;
+        }
+
+        return true;
+    }
+
+    bool computeImpl(Matrix& A, EigenOption& opt) override
+    {
+        INFO("-> compute with Eigen direct linear solver {:s}",
+             EigenOption::getSolverName(opt.solver_type));
         if (!A.isCompressed())
         {
             A.makeCompressed();
@@ -69,13 +124,6 @@ public:
             return false;
         }
 
-        x = solver_.solve(b);
-        if (solver_.info() != Eigen::Success)
-        {
-            ERR("Failed during Eigen linear solve");
-            return false;
-        }
-
         return true;
     }
 
@@ -186,9 +234,9 @@ template <class T_SOLVER>
 class EigenIterativeLinearSolver final : public EigenLinearSolverBase
 {
 public:
-    bool solve(Matrix& A, Vector const& b, Vector& x, EigenOption& opt) override
+    bool computeImpl(Matrix& A, EigenOption& opt) override
     {
-        INFO("-> solve with Eigen iterative linear solver {:s} (precon {:s})",
+        INFO("-> compute with Eigen iterative linear solver {:s} (precon {:s})",
              EigenOption::getSolverName(opt.solver_type),
              EigenOption::getPreconName(opt.precon_type));
         solver_.setTolerance(opt.error_tolerance);
@@ -207,18 +255,32 @@ public:
             T_SOLVER>::setResidualUpdate(opt.residualupdate);
 #endif
 
-        if (!A.isCompressed())
+        // matrix must be copied, because Eigen's linear solver stores a
+        // reference to it cf.
+        // https://eigen.tuxfamily.org/dox/classEigen_1_1IterativeSolverBase.html#a7dfa55c55e82d697bde227696a630914
+        A_ = A;
+
+        if (!A_.isCompressed())
         {
-            A.makeCompressed();
+            A_.makeCompressed();
         }
 
-        solver_.compute(A);
+        solver_.compute(A_);
         if (solver_.info() != Eigen::Success)
         {
             ERR("Failed during Eigen linear solver initialization");
             return false;
         }
 
+        return true;
+    }
+
+    bool solveImpl(Vector const& b, Vector& x, EigenOption& opt) override
+    {
+        INFO("-> solve with Eigen iterative linear solver {:s} (precon {:s})",
+             EigenOption::getSolverName(opt.solver_type),
+             EigenOption::getPreconName(opt.precon_type));
+
         x = solver_.solveWithGuess(b, x);
         INFO("\t iteration: {:d}/{:d}", solver_.iterations(),
              opt.max_iterations);
@@ -235,6 +297,7 @@ public:
 
 private:
     T_SOLVER solver_;
+    Matrix A_;
 #ifdef USE_EIGEN_UNSUPPORTED
     void setRestart(int const restart) { setRestartImpl(solver_, restart); }
     void setL(int const l) { setLImpl(solver_, l); }
@@ -401,34 +464,28 @@ EigenLinearSolver::EigenLinearSolver(std::string const& /*solver_name*/,
 
 EigenLinearSolver::~EigenLinearSolver() = default;
 
-bool EigenLinearSolver::solve(EigenMatrix& A, EigenVector& b, EigenVector& x)
+bool EigenLinearSolver::compute(EigenMatrix& A)
 {
     INFO("------------------------------------------------------------------");
-    INFO("*** Eigen solver computation");
+    INFO("*** Eigen solver compute()");
 
-#ifdef USE_EIGEN_UNSUPPORTED
-    std::unique_ptr<Eigen::IterScaling<EigenMatrix::RawMatrixType>> scal;
-    if (option_.scaling)
-    {
-        INFO("-> scale");
-        scal =
-            std::make_unique<Eigen::IterScaling<EigenMatrix::RawMatrixType>>();
-        scal->computeRef(A.getRawMatrix());
-        b.getRawVector() = scal->LeftScaling().cwiseProduct(b.getRawVector());
-    }
-#endif
-    auto const success = solver_->solve(A.getRawMatrix(), b.getRawVector(),
-                                        x.getRawVector(), option_);
-#ifdef USE_EIGEN_UNSUPPORTED
-    if (scal)
-    {
-        x.getRawVector() = scal->RightScaling().cwiseProduct(x.getRawVector());
-    }
-#endif
+    return solver_->compute(A.getRawMatrix(), option_);
+}
 
+bool EigenLinearSolver::solve(EigenVector& b, EigenVector& x)
+{
     INFO("------------------------------------------------------------------");
+    INFO("*** Eigen solver solve()");
 
-    return success;
+    return solver_->solve(b.getRawVector(), x.getRawVector(), option_);
+
+    INFO("------------------------------------------------------------------");
+}
+
+bool EigenLinearSolver::solve(EigenMatrix& A, EigenVector& b, EigenVector& x)
+{
+    return solver_->compute(A.getRawMatrix(), option_) &&
+           solver_->solve(b.getRawVector(), x.getRawVector(), option_);
 }
 
 }  // namespace MathLib
diff --git a/MathLib/LinAlg/Eigen/EigenLinearSolver.h b/MathLib/LinAlg/Eigen/EigenLinearSolver.h
index 029356e229c0be98b81d3d71c81809caf0ef5adb..668cf08d0c6c50c761b582b02424bf30e2ed1ec6 100644
--- a/MathLib/LinAlg/Eigen/EigenLinearSolver.h
+++ b/MathLib/LinAlg/Eigen/EigenLinearSolver.h
@@ -46,6 +46,24 @@ public:
      */
     EigenOption& getOption() { return option_; }
 
+    /**
+     * Performs the compute() step of the Eigen linear solver.
+     *
+     * I.e., computes the (LU) decomposition in case of a direct solver, or
+     * computes the preconditioner of an iterative solver.
+     */
+    bool compute(EigenMatrix& A);
+
+    /**
+     * Solves the linear system for the given right-hand side \c b and initial
+     * guess \c x.
+     *
+     * \pre compute() must have been called before. (Not necessarily for every
+     * \c x and \c b separately, but for every new/changed matrix A.
+     */
+    bool solve(EigenVector& b, EigenVector& x);
+
+    /// Computes and solves in a single call.
     bool solve(EigenMatrix& A, EigenVector& b, EigenVector& x);
 
 protected:
diff --git a/NumLib/ODESolver/NonlinearSolver.cpp b/NumLib/ODESolver/NonlinearSolver.cpp
index 29b4e69ffeb971c8ce65faa5a1d87df837474cbb..1e57a38f1eae37177964ba2172a8c366543e9606 100644
--- a/NumLib/ODESolver/NonlinearSolver.cpp
+++ b/NumLib/ODESolver/NonlinearSolver.cpp
@@ -24,6 +24,67 @@
 
 namespace NumLib
 {
+namespace detail
+{
+#if !defined(USE_PETSC) && !defined(USE_LIS)
+bool solvePicard(GlobalLinearSolver& linear_solver, GlobalMatrix& A,
+                 GlobalVector& rhs, GlobalVector& x,
+                 bool const compute_necessary)
+{
+    BaseLib::RunTime time_linear_solver;
+    time_linear_solver.start();
+
+    if (compute_necessary)
+    {
+        if (!linear_solver.compute(A))
+        {
+            ERR("Picard: The linear solver failed in the compute() step.");
+            return false;
+        }
+    }
+
+    bool const iteration_succeeded = linear_solver.solve(rhs, x);
+
+    INFO("[time] Linear solver took {:g} s.", time_linear_solver.elapsed());
+
+    if (iteration_succeeded)
+    {
+        return true;
+    }
+
+    ERR("Picard: The linear solver failed in the solve() step.");
+    return false;
+}
+#else
+bool solvePicard(GlobalLinearSolver& linear_solver, GlobalMatrix& A,
+                 GlobalVector& rhs, GlobalVector& x,
+                 bool const compute_necessary)
+{
+    if (!compute_necessary)
+    {
+        WARN(
+            "The performance optimization to skip the linear solver compute() "
+            "step is not implemented for PETSc or LIS linear solvers.");
+    }
+
+    BaseLib::RunTime time_linear_solver;
+    time_linear_solver.start();
+
+    bool const iteration_succeeded = linear_solver.solve(A, rhs, x);
+
+    INFO("[time] Linear solver took {:g} s.", time_linear_solver.elapsed());
+
+    if (iteration_succeeded)
+    {
+        return true;
+    }
+
+    ERR("Picard: The linear solver failed in the solve() step.");
+    return false;
+}
+#endif
+}  // namespace detail
+
 void NonlinearSolver<NonlinearSolverTag::Picard>::
     calculateNonEquilibriumInitialResiduum(
         std::vector<GlobalVector*> const& x,
@@ -128,17 +189,11 @@ NonlinearSolverStatus NonlinearSolver<NonlinearSolverTag::Picard>::solve(
             _convergence_criterion->checkResidual(res);
         }
 
-        BaseLib::RunTime time_linear_solver;
-        time_linear_solver.start();
         bool iteration_succeeded =
-            _linear_solver.solve(A, rhs, *x_new[process_id]);
-        INFO("[time] Linear solver took {:g} s.", time_linear_solver.elapsed());
+            detail::solvePicard(_linear_solver, A, rhs, x_new_process,
+                                sys.linearSolverNeedsToCompute());
 
-        if (!iteration_succeeded)
-        {
-            ERR("Picard: The linear solver failed.");
-        }
-        else
+        if (iteration_succeeded)
         {
             if (postIterationCallback)
             {
diff --git a/NumLib/ODESolver/NonlinearSystem.h b/NumLib/ODESolver/NonlinearSystem.h
index 138cd7e4d9fefbf24bd48b77f91b7e90263abbba..480db9dce72f842104b1fa4e406482e45b31223c 100644
--- a/NumLib/ODESolver/NonlinearSystem.h
+++ b/NumLib/ODESolver/NonlinearSystem.h
@@ -126,6 +126,10 @@ public:
     //! \pre computeKnownSolutions() must have been called before.
     virtual void applyKnownSolutionsPicard(GlobalMatrix& A, GlobalVector& rhs,
                                            GlobalVector& x) const = 0;
+
+    //! Returns whether the assembled matrix \f$A\f$ has changed and the linear
+    //! solver must perform the MathLib::EigenLinearSolver::compute() step.
+    virtual bool linearSolverNeedsToCompute() const = 0;
 };
 
 //! @}
diff --git a/NumLib/ODESolver/ODESystem.h b/NumLib/ODESolver/ODESystem.h
index f98a11310816ce4a8990a9f6b8c3c0391df55c3e..91abd358e73afcc57a176d8525a0eaedaaa4338c 100644
--- a/NumLib/ODESolver/ODESystem.h
+++ b/NumLib/ODESolver/ODESystem.h
@@ -77,6 +77,14 @@ public:
     virtual void updateConstraints(GlobalVector& /*lower*/,
                                    GlobalVector& /*upper*/,
                                    int const /*process_id*/){};
+
+    //! Indicates whether the assembled matrices change only upon timestep
+    //! change. This enables some optimizations in the linear solver, but the
+    //! user must be 100 % sure that she uses this option correctly.
+    virtual bool shouldLinearSolverComputeOnlyUponTimestepChange() const
+    {
+        return false;
+    }
 };
 
 /*! Interface for a first-order implicit quasi-linear ODE.
diff --git a/NumLib/ODESolver/TimeDiscretization.h b/NumLib/ODESolver/TimeDiscretization.h
index fe6b71d8f50b324647d554a4d643c3d00f2dba34..65bd3313f8d9382f2a0b296151c08483c2ec7a53 100644
--- a/NumLib/ODESolver/TimeDiscretization.h
+++ b/NumLib/ODESolver/TimeDiscretization.h
@@ -128,6 +128,9 @@ public:
     //! assembled.
     virtual double getCurrentTimeIncrement() const = 0;
 
+    //! Returns the value of \f$\Delta t\f$ from the previous time step.
+    virtual double getPreviousTimeIncrement() const = 0;
+
     //! Returns \f$ x_O \f$.
     virtual void getWeightedOldX(
         GlobalVector& y, GlobalVector const& x_old) const = 0;  // = x_old
@@ -142,12 +145,14 @@ public:
     void setInitialState(const double t0) override { _t = t0; }
     void nextTimestep(const double t, const double delta_t) override
     {
+        _delta_t_prev = _delta_t;
         _t = t;
         _delta_t = delta_t;
     }
 
     double getCurrentTime() const override { return _t; }
     double getCurrentTimeIncrement() const override { return _delta_t; }
+    double getPreviousTimeIncrement() const override { return _delta_t_prev; }
     void getWeightedOldX(GlobalVector& y,
                          GlobalVector const& x_old) const override;
 
@@ -155,6 +160,9 @@ private:
     double _t = std::numeric_limits<double>::quiet_NaN();  //!< \f$ t_C \f$
     double _delta_t =
         std::numeric_limits<double>::quiet_NaN();  //!< the timestep size
+
+    //! The timestep size of the previous timestep.
+    double _delta_t_prev = std::numeric_limits<double>::quiet_NaN();
 };
 
 //! @}
diff --git a/NumLib/ODESolver/TimeDiscretizedODESystem.h b/NumLib/ODESolver/TimeDiscretizedODESystem.h
index eeed8a7ea18137750ae525c6e7e1d52865b766b9..86bcc78d7ed842ab5a2fad9e9bf4d97e7be06a69 100644
--- a/NumLib/ODESolver/TimeDiscretizedODESystem.h
+++ b/NumLib/ODESolver/TimeDiscretizedODESystem.h
@@ -234,6 +234,13 @@ public:
         return _ode.getMatrixSpecifications(process_id);
     }
 
+    bool linearSolverNeedsToCompute() const override
+    {
+        return !_ode.shouldLinearSolverComputeOnlyUponTimestepChange() ||
+               _time_disc.getCurrentTimeIncrement() !=
+                   _time_disc.getPreviousTimeIncrement();
+    }
+
 private:
     ODE& _ode;             //!< ode the ODE being wrapped
     TimeDisc& _time_disc;  //!< the time discretization to being used
diff --git a/ProcessLib/ComponentTransport/ComponentTransportProcess.cpp b/ProcessLib/ComponentTransport/ComponentTransportProcess.cpp
index 3e414ece1b6ef527eaa718a9bfaaa8ce310916fc..294389bafc8bc442555b6b27b5fc1e0444b995ee 100644
--- a/ProcessLib/ComponentTransport/ComponentTransportProcess.cpp
+++ b/ProcessLib/ComponentTransport/ComponentTransportProcess.cpp
@@ -44,15 +44,37 @@ ComponentTransportProcess::ComponentTransportProcess(
     std::unique_ptr<ProcessLib::SurfaceFluxData>&& surfaceflux,
     std::unique_ptr<ChemistryLib::ChemicalSolverInterface>&&
         chemical_solver_interface,
-    bool const is_linear)
+    bool const is_linear,
+    bool const ls_compute_only_upon_timestep_change)
     : Process(std::move(name), mesh, std::move(jacobian_assembler), parameters,
               integration_order, std::move(process_variables),
               std::move(secondary_variables), use_monolithic_scheme),
       _process_data(std::move(process_data)),
       _surfaceflux(std::move(surfaceflux)),
       _chemical_solver_interface(std::move(chemical_solver_interface)),
-      _asm_mat_cache{is_linear, use_monolithic_scheme}
+      _asm_mat_cache{is_linear, use_monolithic_scheme},
+      _ls_compute_only_upon_timestep_change{
+          ls_compute_only_upon_timestep_change}
 {
+    if (ls_compute_only_upon_timestep_change)
+    {
+        if (!is_linear)
+        {
+            OGS_FATAL(
+                "Using the linear solver compute() method only upon timestep "
+                "change only makes sense for linear model equations.");
+        }
+
+        WARN(
+            "You specified that the ComponentTransport linear solver will do "
+            "the compute() step only upon timestep change. This is an expert "
+            "option. It is your responsibility to ensure that "
+            "the conditions for the correct use of this feature are met! "
+            "Otherwise OGS might compute garbage without being recognized. "
+            "There is no "
+            "safety net!");
+    }
+
     _residua.push_back(MeshLib::getOrCreateMeshProperty<double>(
         mesh, "LiquidMassFlowRate", MeshLib::MeshItemType::Node, 1));
 
diff --git a/ProcessLib/ComponentTransport/ComponentTransportProcess.h b/ProcessLib/ComponentTransport/ComponentTransportProcess.h
index 930941a473e23647a9fa5322e4574f66aa888d4a..79738129848e058c0a493738f955116923e24ecc 100644
--- a/ProcessLib/ComponentTransport/ComponentTransportProcess.h
+++ b/ProcessLib/ComponentTransport/ComponentTransportProcess.h
@@ -111,7 +111,8 @@ public:
         std::unique_ptr<ProcessLib::SurfaceFluxData>&& surfaceflux,
         std::unique_ptr<ChemistryLib::ChemicalSolverInterface>&&
             chemical_solver_interface,
-        bool const is_linear);
+        bool const is_linear,
+        bool const ls_compute_only_upon_timestep_change);
 
     //! \name ODESystem interface
     //! @{
@@ -141,6 +142,11 @@ public:
                                      const double dt,
                                      int const process_id) override;
 
+    bool shouldLinearSolverComputeOnlyUponTimestepChange() const override
+    {
+        return _ls_compute_only_upon_timestep_change;
+    }
+
 private:
     void initializeConcreteProcess(
         NumLib::LocalToGlobalIndexMap const& dof_table,
@@ -176,6 +182,8 @@ private:
     std::vector<MeshLib::PropertyVector<double>*> _residua;
 
     AssembledMatrixCache _asm_mat_cache;
+
+    bool const _ls_compute_only_upon_timestep_change;
 };
 
 }  // namespace ComponentTransport
diff --git a/ProcessLib/ComponentTransport/CreateComponentTransportProcess.cpp b/ProcessLib/ComponentTransport/CreateComponentTransportProcess.cpp
index 0082703f4366ac86a6b40c75a928b85d67fe078f..d10f328f2fc5be39af90ecedf00593bbd2f6d22f 100644
--- a/ProcessLib/ComponentTransport/CreateComponentTransportProcess.cpp
+++ b/ProcessLib/ComponentTransport/CreateComponentTransportProcess.cpp
@@ -256,6 +256,11 @@ std::unique_ptr<Process> createComponentTransportProcess(
         //! \ogs_file_param{prj__processes__process__ComponentTransport__is_linear}
         config.getConfigParameter("is_linear", false);
 
+    auto const ls_compute_only_upon_timestep_change =
+        //! \ogs_file_param{prj__processes__process__ComponentTransport__linear_solver_compute_only_upon_timestep_change}
+        config.getConfigParameter(
+            "linear_solver_compute_only_upon_timestep_change", false);
+
     auto const rotation_matrices = MeshLib::getElementRotationMatrices(
         mesh_space_dimension, mesh.getDimension(), mesh.getElements());
     std::vector<Eigen::VectorXd> projected_specific_body_force_vectors;
@@ -298,7 +303,8 @@ std::unique_ptr<Process> createComponentTransportProcess(
         integration_order, std::move(process_variables),
         std::move(process_data), std::move(secondary_variables),
         use_monolithic_scheme, std::move(surfaceflux),
-        std::move(chemical_solver_interface), is_linear);
+        std::move(chemical_solver_interface), is_linear,
+        ls_compute_only_upon_timestep_change);
 }
 
 }  // namespace ComponentTransport
diff --git a/ProcessLib/ComponentTransport/Tests.cmake b/ProcessLib/ComponentTransport/Tests.cmake
index d0df696eaa09d8cca9bf5435877832b19daed297..9caa62c95a27e69a615878618a04c24faadb4cde 100644
--- a/ProcessLib/ComponentTransport/Tests.cmake
+++ b/ProcessLib/ComponentTransport/Tests.cmake
@@ -788,11 +788,33 @@ if (NOT OGS_USE_MPI)
     OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/CationExchange/exchange.prj RUNTIME 60)
     OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/CationExchange/exchangeAndSurface.prj RUNTIME 33)
     OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/1d_decay_chain_OS.prj RUNTIME 2000)
+
+    # several variations of 1d_decay_chain_GIA
     OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA.prj RUNTIME 40)
-    OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once.xml RUNTIME 10)
+    OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA.xml RUNTIME 10)
+    OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml RUNTIME 4)
+
+    # further variations of 1d_decay_chain_GIA with Eigen's SparseLU solver
+    OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA.xml RUNTIME 40)
+    OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA.xml RUNTIME 10)
+    OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml RUNTIME 4)
+
+    # variation with changing timestep size
+    OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA.xml RUNTIME 40)
+    OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA.xml RUNTIME 10)
+    OgsTest(PROJECTFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml RUNTIME 4)
+
     OgsTest(PROJECTFILE Parabolic/ComponentTransport/ThermalDiffusion/TemperatureField_transport.prj RUNTIME 27)
 endif()
 
+if(NOT OGS_USE_PETSC)
+    NotebookTest(
+        NOTEBOOKFILE Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb
+        RUNTIME 200
+        SKIP_WEB
+    )
+endif()
+
 AddTest(
     NAME 2D_ReactiveMassTransport_Phreeqc_KineticReactantBlockTest_AllAsComponents
     PATH Parabolic/ComponentTransport/ReactiveTransport/KineticReactant_AllAsComponents
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once.xml b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once.xml
deleted file mode 100644
index d5fb7813b27c990432ba53df6a52926bdc5d0453..0000000000000000000000000000000000000000
--- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once.xml
+++ /dev/null
@@ -1,95 +0,0 @@
-<?xml version='1.0' encoding='ISO-8859-1'?>
-<OpenGeoSysProjectDiff base_file="1d_decay_chain_GIA.prj">
-    <add sel="/*/processes/process">
-        <is_linear>true</is_linear>
-    </add>
-
-    <remove sel="/*/test_definition" />
-
-    <replace sel="/*/time_loop/output/prefix/text()">
-        1d_decay_chain_GIA_asm_only_once
-    </replace>
-
-    <add sel="/*">
-        <test_definition>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[Cm-247]</field>
-            <absolute_tolerance>2e-8</absolute_tolerance>
-            <relative_tolerance>1e-10</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[Am-243]</field>
-            <absolute_tolerance>2e-8</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[Pu-239]</field>
-            <absolute_tolerance>2e-8</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[U-235]</field>
-            <absolute_tolerance>2e-8</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[Pa-231]</field>
-            <absolute_tolerance>2e-8</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[Ac-227]</field>
-            <absolute_tolerance>1e-8</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>LiquidMassFlowRate</field>
-            <absolute_tolerance>1e-10</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[Cm-247]FlowRate</field>
-            <absolute_tolerance>1e-10</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[Cm-247]FlowRate</field>
-            <absolute_tolerance>1e-10</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[Pu-239]FlowRate</field>
-            <absolute_tolerance>1e-10</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[U-235]FlowRate</field>
-            <absolute_tolerance>1e-10</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[Pa-231]FlowRate</field>
-            <absolute_tolerance>1e-10</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        <vtkdiff>
-            <regex>1d_decay_chain_GIA_asm_only_once_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
-            <field>[Ac-227]FlowRate</field>
-            <absolute_tolerance>1e-10</absolute_tolerance>
-            <relative_tolerance>1e-16</relative_tolerance>
-        </vtkdiff>
-        </test_definition>
-    </add>
-</OpenGeoSysProjectDiff>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_0_t_0.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_0_t_0.000000.vtu
deleted file mode 120000
index 6f5000a8bc781ca0276ea733d02dabfdab5e8e8f..0000000000000000000000000000000000000000
--- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_0_t_0.000000.vtu
+++ /dev/null
@@ -1 +0,0 @@
-1d_decay_chain_GIA_ts_0_t_0.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_1000_t_3153600000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_1000_t_3153600000000.000000.vtu
deleted file mode 120000
index fe539d52010a57406b6e77b0e047d8ce71207ccc..0000000000000000000000000000000000000000
--- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_1000_t_3153600000000.000000.vtu
+++ /dev/null
@@ -1 +0,0 @@
-1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_100_t_315360000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_100_t_315360000000.000000.vtu
deleted file mode 120000
index a2ba3c1ef9788fa357c64114ad6d576286db8f86..0000000000000000000000000000000000000000
--- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_100_t_315360000000.000000.vtu
+++ /dev/null
@@ -1 +0,0 @@
-1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_10_t_31536000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_10_t_31536000000.000000.vtu
deleted file mode 120000
index afa18784cfd3b774654da070a300c0741445b6dd..0000000000000000000000000000000000000000
--- a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/1d_decay_chain_GIA_asm_only_once_ts_10_t_31536000000.000000.vtu
+++ /dev/null
@@ -1 +0,0 @@
-1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..692b44a36478dc3c1dcc9ae069a1f38de032117e
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA.xml b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA.xml
new file mode 100644
index 0000000000000000000000000000000000000000..d9273c291af252636b54f34acd8e9693cf4858b4
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA.xml
@@ -0,0 +1,10 @@
+<?xml version='1.0' encoding='ISO-8859-1'?>
+<OpenGeoSysProjectDiff base_file="../1d_decay_chain_GIA.prj">
+    <remove sel="/*/linear_solvers/linear_solver/eigen" />
+
+    <add sel="/*/linear_solvers/linear_solver">
+        <eigen>
+            <solver_type>SparseLU</solver_type>
+        </eigen>
+    </add>
+</OpenGeoSysProjectDiff>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_0_t_0.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b647f53816b385f3d956ee89f521d30c650f2f2a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_0_t_0.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b65363fc6453a5db9e4eb5e255973ef49bcd7d61
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..042d5449fb60578db6028ab9acc312bd6f55c991
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..fb3bca1bca2ff0bca433056588512336aa0e76a7
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_ReactiveDomain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_ReactiveDomain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..13f06137f4412db5e3ea84848af7f3ae8e27fc15
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_ReactiveDomain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_ReactiveDomain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_upstream.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_upstream.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..436686ec91dbcfd3cf92c096e3ea2dedf07cab6a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU/1d_decay_chain_upstream.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_upstream.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..692b44a36478dc3c1dcc9ae069a1f38de032117e
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA.xml b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA.xml
new file mode 100644
index 0000000000000000000000000000000000000000..1c821430cdefb9bd5d63d36961bd1d8506606b3f
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA.xml
@@ -0,0 +1,14 @@
+<?xml version='1.0' encoding='ISO-8859-1'?>
+<OpenGeoSysProjectDiff base_file="../1d_decay_chain_GIA.prj">
+    <add sel="/*/processes/process">
+        <is_linear>true</is_linear>
+    </add>
+
+    <remove sel="/*/linear_solvers/linear_solver/eigen" />
+
+    <add sel="/*/linear_solvers/linear_solver">
+        <eigen>
+            <solver_type>SparseLU</solver_type>
+        </eigen>
+    </add>
+</OpenGeoSysProjectDiff>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_0_t_0.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b647f53816b385f3d956ee89f521d30c650f2f2a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_0_t_0.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b65363fc6453a5db9e4eb5e255973ef49bcd7d61
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..042d5449fb60578db6028ab9acc312bd6f55c991
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..fb3bca1bca2ff0bca433056588512336aa0e76a7
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_ReactiveDomain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_ReactiveDomain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..13f06137f4412db5e3ea84848af7f3ae8e27fc15
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_ReactiveDomain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_ReactiveDomain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_upstream.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_upstream.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..436686ec91dbcfd3cf92c096e3ea2dedf07cab6a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear/1d_decay_chain_upstream.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_upstream.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..692b44a36478dc3c1dcc9ae069a1f38de032117e
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml
new file mode 100644
index 0000000000000000000000000000000000000000..55a5907a6c19490da9451daf100ee400f39a75b4
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml
@@ -0,0 +1,15 @@
+<?xml version='1.0' encoding='ISO-8859-1'?>
+<OpenGeoSysProjectDiff base_file="../1d_decay_chain_GIA.prj">
+    <add sel="/*/processes/process">
+        <is_linear>true</is_linear>
+        <linear_solver_compute_only_upon_timestep_change>true</linear_solver_compute_only_upon_timestep_change>
+    </add>
+
+    <remove sel="/*/linear_solvers/linear_solver/eigen" />
+
+    <add sel="/*/linear_solvers/linear_solver">
+        <eigen>
+            <solver_type>SparseLU</solver_type>
+        </eigen>
+    </add>
+</OpenGeoSysProjectDiff>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_0_t_0.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b647f53816b385f3d956ee89f521d30c650f2f2a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_0_t_0.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b65363fc6453a5db9e4eb5e255973ef49bcd7d61
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..042d5449fb60578db6028ab9acc312bd6f55c991
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..fb3bca1bca2ff0bca433056588512336aa0e76a7
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_ReactiveDomain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_ReactiveDomain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..13f06137f4412db5e3ea84848af7f3ae8e27fc15
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_ReactiveDomain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_ReactiveDomain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_upstream.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_upstream.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..436686ec91dbcfd3cf92c096e3ea2dedf07cab6a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_upstream.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_upstream.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..692b44a36478dc3c1dcc9ae069a1f38de032117e
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA.xml b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA.xml
new file mode 100644
index 0000000000000000000000000000000000000000..35a742eda51958d12409e5d8b2fb03992a4d937a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA.xml
@@ -0,0 +1,6 @@
+<?xml version='1.0' encoding='ISO-8859-1'?>
+<OpenGeoSysProjectDiff base_file="../1d_decay_chain_GIA.prj">
+    <add sel="/*/processes/process">
+        <is_linear>true</is_linear>
+    </add>
+</OpenGeoSysProjectDiff>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_0_t_0.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b647f53816b385f3d956ee89f521d30c650f2f2a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_0_t_0.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b65363fc6453a5db9e4eb5e255973ef49bcd7d61
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..042d5449fb60578db6028ab9acc312bd6f55c991
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..fb3bca1bca2ff0bca433056588512336aa0e76a7
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_ReactiveDomain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_ReactiveDomain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..13f06137f4412db5e3ea84848af7f3ae8e27fc15
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_ReactiveDomain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_ReactiveDomain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_upstream.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_upstream.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..436686ec91dbcfd3cf92c096e3ea2dedf07cab6a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear/1d_decay_chain_upstream.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_upstream.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..692b44a36478dc3c1dcc9ae069a1f38de032117e
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml
new file mode 100644
index 0000000000000000000000000000000000000000..55aaf9b19f68251f21824cfa0b0e76b240cbb904
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml
@@ -0,0 +1,7 @@
+<?xml version='1.0' encoding='ISO-8859-1'?>
+<OpenGeoSysProjectDiff base_file="../1d_decay_chain_GIA.prj">
+    <add sel="/*/processes/process">
+        <is_linear>true</is_linear>
+        <linear_solver_compute_only_upon_timestep_change>true</linear_solver_compute_only_upon_timestep_change>
+    </add>
+</OpenGeoSysProjectDiff>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_0_t_0.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b647f53816b385f3d956ee89f521d30c650f2f2a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_0_t_0.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b65363fc6453a5db9e4eb5e255973ef49bcd7d61
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_1000_t_3153600000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..042d5449fb60578db6028ab9acc312bd6f55c991
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_100_t_315360000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..fb3bca1bca2ff0bca433056588512336aa0e76a7
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_GIA_ts_10_t_31536000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_ReactiveDomain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_ReactiveDomain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..13f06137f4412db5e3ea84848af7f3ae8e27fc15
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_ReactiveDomain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_ReactiveDomain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_upstream.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_upstream.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..436686ec91dbcfd3cf92c096e3ea2dedf07cab6a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/is_linear_compute_only_on_dt_change/1d_decay_chain_upstream.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_upstream.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..85d9a2f7a8963f159b85011f8e3f432c56c7a1de
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/performance_measurements.ipynb
@@ -0,0 +1,1079 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dd267901-843a-4f53-b161-421cce229047",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from ogs6py.log_parser.log_parser import parse_file\n",
+    "import pandas as pd\n",
+    "from ogs6py.log_parser.common_ogs_analyses import (\n",
+    "    fill_ogs_context,\n",
+    "    analysis_time_step,\n",
+    "    analysis_convergence_newton_iteration,\n",
+    "    analysis_convergence_coupling_iteration,\n",
+    "    analysis_simulation_termination,\n",
+    "    time_step_vs_iterations,\n",
+    ")\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import subprocess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "91dcc538-638a-4dc7-9a56-0f8719bc035e",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"OMP_NUM_THREADS\"] = \"1\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b5ea0453-4e77-43d7-a3c4-8c6ca6645583",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "base output directory is _out\n"
+     ]
+    }
+   ],
+   "source": [
+    "out_dir_base = os.environ.get(\"OGS_TESTRUNNER_OUT_DIR\", \"_out\")\n",
+    "print(\"base output directory is\", out_dir_base)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "661a8be6-1118-496c-98ce-0bf1968bb492",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_out_dir(case):\n",
+    "    return os.path.join(out_dir_base, os.path.dirname(case))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de8a8e97-f0c2-43c1-afec-739748abb1ef",
+   "metadata": {},
+   "source": [
+    "# Considered cases\n",
+    "\n",
+    "1. base case\n",
+    "2. `<is_linear>true</is_linear>`\n",
+    "    * requires that global matrices `M`, `K` and right-hand side vector `b` assembled by the `ComponentTransport` process do not change over time (and do not depend on the solution)\n",
+    "    * requires that BCs and STs do not depend on the solution\n",
+    "    * performs only **one non-linear iteration per timestep**\n",
+    "    * `ComponentTransport` process **caches assembled global matrices** (no re-assembly necessary)\n",
+    "3. `<is_linear>true</is_linear>` and `<linear_solver_compute_only_upon_timestep_change>true</linear_solver_compute_only_upon_timestep_change>`\n",
+    "    * requires additionally, that also the BC and ST contributions to the global matrices do not change over time (no time- or solution-dependent BCs or STs!)\n",
+    "    * LU decomposition or preconditioner is **computed only upon timestep change**\n",
+    "    * only implemented for linear solvers from the Eigen library, so far\n",
+    "\n",
+    "* both for an iterative (BiCGSTAB with ILUT preconditioner) and a direct linear solver (LU)\n",
+    "* note: ILUT is a rather expensive preconditioner. Optimization (3.) might accelerate ILUT much more than a cheaper preconditioner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "edea9300-105e-4674-acbb-1259a78bdcb5",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cases = [\n",
+    "    [\"1 base\", \"./1d_decay_chain_GIA.prj\"],\n",
+    "    [\"2 linear\", \"is_linear/1d_decay_chain_GIA.xml\"],\n",
+    "    [\"3 linear & dt\", \"is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml\"],\n",
+    "    [\"4 LU\", \"SparseLU/1d_decay_chain_GIA.xml\"],\n",
+    "    [\"5 LU & linear\", \"SparseLU_is_linear/1d_decay_chain_GIA.xml\"],\n",
+    "    [\n",
+    "        \"6 LU & linear & dt\",\n",
+    "        \"SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml\",\n",
+    "    ],\n",
+    "    [\"7 varying dt\", \"varying_dt/1d_decay_chain_GIA.xml\"],\n",
+    "    [\"8 varying dt & linear\", \"varying_dt_is_linear/1d_decay_chain_GIA.xml\"],\n",
+    "    [\n",
+    "        \"9 varying dt & linear & dt\",\n",
+    "        \"varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml\",\n",
+    "    ],\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2935acbd-f9b2-4abc-b95b-ccdd9c425380",
+   "metadata": {},
+   "source": [
+    "# Running OGS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e94565dc-eab8-4b04-93db-38872403c8f9",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "running case ./1d_decay_chain_GIA.prj\n",
+      "running case is_linear/1d_decay_chain_GIA.xml\n",
+      "running case is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml\n",
+      "running case SparseLU/1d_decay_chain_GIA.xml\n",
+      "running case SparseLU_is_linear/1d_decay_chain_GIA.xml\n",
+      "running case SparseLU_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml\n",
+      "running case varying_dt/1d_decay_chain_GIA.xml\n",
+      "running case varying_dt_is_linear/1d_decay_chain_GIA.xml\n",
+      "running case varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, case in cases:\n",
+    "    outdir = get_out_dir(case)\n",
+    "    if not os.path.exists(outdir):\n",
+    "        os.makedirs(outdir)\n",
+    "        with open(os.path.join(outdir, \".gitignore\"), \"w\") as fh:\n",
+    "            fh.write(\"*\\n\")\n",
+    "\n",
+    "    print(f\"running case {case}\")\n",
+    "    with open(os.path.join(outdir, \"ogs-out.txt\"), \"w\") as fh:\n",
+    "        subprocess.run(\n",
+    "            [\"ogs\", \"-o\", outdir, case], check=True, stdout=fh, stderr=subprocess.STDOUT\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1308c845-2c7b-41eb-9283-c3abf489bb26",
+   "metadata": {},
+   "source": [
+    "# Process log files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3a1ca09c-d734-4df8-bb61-b041c86eb4e9",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:129: FutureWarning: The provided callable <function max at 0x7f4b643e4ae0> is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'max' instead.\n",
+      "  pt = df.pivot_table([\"iteration_number\"], [\"time_step\"], aggfunc=np.max)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:129: FutureWarning: The provided callable <function max at 0x7f4b643e4ae0> is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'max' instead.\n",
+      "  pt = df.pivot_table([\"iteration_number\"], [\"time_step\"], aggfunc=np.max)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:129: FutureWarning: The provided callable <function max at 0x7f4b643e4ae0> is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'max' instead.\n",
+      "  pt = df.pivot_table([\"iteration_number\"], [\"time_step\"], aggfunc=np.max)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:129: FutureWarning: The provided callable <function max at 0x7f4b643e4ae0> is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'max' instead.\n",
+      "  pt = df.pivot_table([\"iteration_number\"], [\"time_step\"], aggfunc=np.max)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:129: FutureWarning: The provided callable <function max at 0x7f4b643e4ae0> is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'max' instead.\n",
+      "  pt = df.pivot_table([\"iteration_number\"], [\"time_step\"], aggfunc=np.max)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:129: FutureWarning: The provided callable <function max at 0x7f4b643e4ae0> is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'max' instead.\n",
+      "  pt = df.pivot_table([\"iteration_number\"], [\"time_step\"], aggfunc=np.max)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:129: FutureWarning: The provided callable <function max at 0x7f4b643e4ae0> is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'max' instead.\n",
+      "  pt = df.pivot_table([\"iteration_number\"], [\"time_step\"], aggfunc=np.max)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:129: FutureWarning: The provided callable <function max at 0x7f4b643e4ae0> is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'max' instead.\n",
+      "  pt = df.pivot_table([\"iteration_number\"], [\"time_step\"], aggfunc=np.max)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:170: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['time_step'] = df.groupby('mpi_process')[['time_step']].fillna(method='ffill').fillna(value=0)\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:173: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['iteration_number'] = df.groupby('mpi_process')[['iteration_number']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrameGroupBy.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:180: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+      "  df['process'] = df.groupby('mpi_process')[['process']].fillna(method='bfill')\n",
+      "/home/lehmannc/prog/py-venvs/ogs-local-release-build/lib/python3.11/site-packages/ogs6py/log_parser/common_ogs_analyses.py:129: FutureWarning: The provided callable <function max at 0x7f4b643e4ae0> is currently using DataFrameGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'max' instead.\n",
+      "  pt = df.pivot_table([\"iteration_number\"], [\"time_step\"], aggfunc=np.max)\n"
+     ]
+    }
+   ],
+   "source": [
+    "stats = []\n",
+    "exec_times = []\n",
+    "\n",
+    "for name, case in cases:\n",
+    "    outdir = get_out_dir(case)\n",
+    "    logfile = os.path.join(outdir, \"ogs-out.txt\")\n",
+    "\n",
+    "    records = parse_file(logfile)\n",
+    "\n",
+    "    dfa = pd.DataFrame(records)\n",
+    "    dfb = fill_ogs_context(dfa)\n",
+    "    dfc = time_step_vs_iterations(dfb)\n",
+    "    dfd = analysis_time_step(dfa)\n",
+    "    dfd = dfd.droplevel(\"mpi_process\")\n",
+    "    dfe = dfd.join(dfc)\n",
+    "    dfe.drop(0, inplace=True)  # remove timestep 0 (only output)\n",
+    "\n",
+    "    exec_times.append(dfa[\"execution_time\"].max())\n",
+    "\n",
+    "    stats.append(dfe)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b633bfa5-0059-456d-b4ef-0da16eff0b26",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# merge stats dataframes\n",
+    "df_stats = pd.DataFrame()\n",
+    "# df_stats = stats[0].copy()\n",
+    "# df_stats[\"case_name\"] = cases[0][0]\n",
+    "\n",
+    "for (name, case), stat in zip(cases, stats):\n",
+    "    tmp = stat.copy()\n",
+    "    tmp[\"case_name\"] = name\n",
+    "    df_stats = pd.concat([df_stats, tmp])\n",
+    "\n",
+    "df_stats.reset_index(inplace=True)\n",
+    "df_stats.set_index([\"case_name\", \"time_step\"], inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "135c2645-b3d8-42a8-bf46-1b6edf07c4b0",
+   "metadata": {},
+   "source": [
+    "# Number of timesteps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3c5a05f6-533b-43fe-b3a6-ed64b5e2637b",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = (\n",
+    "    df_stats.reset_index(\"time_step\")\n",
+    "    .groupby(\"case_name\")\n",
+    "    .max()\n",
+    "    .plot(\n",
+    "        y=\"time_step\",\n",
+    "        kind=\"bar\",\n",
+    "        legend=False,\n",
+    "        rot=20,\n",
+    "        xlabel=\"case\",\n",
+    "        ylabel=\"number of timesteps\",\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "for container in ax.containers:\n",
+    "    ax.bar_label(container)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b69b72f7-1d89-4256-beba-94d875ac5506",
+   "metadata": {},
+   "source": [
+    "# Non-linear solver iterations per timestep"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8f2031ac-7f44-4d08-8188-915c7827187d",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_it_num = df_stats[\"iteration_number\"].groupby(\"case_name\").agg([\"min\", \"max\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0fd4dd3b-6976-4254-9491-d7045fefbb49",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ax = df_it_num.plot();\n",
+    "ax = df_it_num.plot(kind=\"bar\")\n",
+    "ax.set_ylabel(\"non-linear solver iterations per timestep\")\n",
+    "ax.set_xlabel(\"case\")\n",
+    "ax.set_xticklabels(ax.get_xticklabels(), rotation=15, ha=\"right\")\n",
+    "ax.set_yticks([0, 1, 2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "4216fbb1-4cbe-4ef5-928e-41bbabb5b520",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "assert np.all(df_it_num[\"min\"] == df_it_num[\"max\"])\n",
+    "# attention: depends on the order of cases!\n",
+    "assert np.all(df_it_num[\"max\"].values == [2, 1, 1, 2, 1, 1, 2, 1, 1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "058305aa-e717-4dcf-989b-b58591494d87",
+   "metadata": {},
+   "source": [
+    "* with `<is_linear>true<is_linear>` only one non-linear iteration is done per timestep."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a141c843-60f9-4dcf-a917-46b3b849f382",
+   "metadata": {},
+   "source": [
+    "# Total execution time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "240e25d1-1ee7-4cb8-9423-e2311d1c4208",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 0, 'case')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.bar([i for i in range(len(cases))], height=exec_times)\n",
+    "ax.set_xticks([i for i in range(len(cases))])\n",
+    "ax.set_xticklabels([name for name, case in cases], rotation=15, ha=\"right\")\n",
+    "ax.set_ylabel(\"execution time / s\")\n",
+    "ax.grid(axis=\"y\", ls=\":\")\n",
+    "ax.set_xlabel(\"case\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dc83bbc6-a1c4-45f7-8057-988be5550b70",
+   "metadata": {},
+   "source": [
+    "# Duration of timesteps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5593ee90-1ca4-439c-b25d-a8dea9c50304",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs = df_stats.boxplot(\n",
+    "    column=[\n",
+    "        \"time_step_solution_time\",\n",
+    "        \"linear_solver_time\",\n",
+    "        \"assembly_time\",\n",
+    "        \"dirichlet_time\",\n",
+    "    ],\n",
+    "    by=\"case_name\",\n",
+    "    showfliers=False,\n",
+    "    notch=False,\n",
+    "    whis=(5, 95),\n",
+    "    showmeans=True,\n",
+    "    meanprops={\"marker\": \"x\", \"color\": \"r\"},\n",
+    "    sharex=False,\n",
+    "    sharey=False,\n",
+    "    rot=15,\n",
+    ")\n",
+    "# ax.set_xlabel(\"case\");\n",
+    "# ax.set_ylabel(\"timestep solution time / s\");\n",
+    "for ax in axs.ravel():\n",
+    "    # ax.set_xticks(ax.get_xticks())\n",
+    "    ax.set_xticklabels(ax.get_xticklabels(), rotation=15, ha=\"right\")\n",
+    "    ax.set_xlabel(\"\")\n",
+    "    ax.set_ylabel(\"$t$ / s\")\n",
+    "    ax.set_ylim(0)\n",
+    "    ax.grid(axis=\"y\", ls=\":\")\n",
+    "    ax.grid(axis=\"x\", ls=\"\")\n",
+    "    ax.set_title(ax.get_title() + \" (per timestep)\")\n",
+    "# ax.set_title(\"\");\n",
+    "# ax.set_suptitle(\"\");\n",
+    "fig = axs.ravel()[0].get_figure()\n",
+    "fig.suptitle(\"quantiles: [5% 25% 50% 75% 95%]; mean = cross\")\n",
+    "fig.set_size_inches(12, 12)\n",
+    "fig.subplots_adjust(wspace=0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bb63753-be8f-4158-a478-14fe38505dd4",
+   "metadata": {},
+   "source": [
+    "# Speedups"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1ea68adb-3aef-433b-b9fd-69e01fdfe6ee",
+   "metadata": {},
+   "source": [
+    "## Total execution time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "d3b9512d-e7f9-40e4-9424-30cde9b21703",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "speedups = np.array(exec_times)\n",
+    "# Attention: relies on ordering of cases!\n",
+    "speedups[:3] = np.max(speedups[:3]) / speedups[:3]\n",
+    "speedups[3:6] = np.max(speedups[3:6]) / speedups[3:6]\n",
+    "speedups[6:] = np.max(speedups[6:]) / speedups[6:]\n",
+    "assert len(speedups) == 9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "54de0dc5-0ff8-4169-a751-85aef7c6c595",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.lines.Line2D at 0x7f4b69162e10>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "ax.bar([i + i // 3 for i in range(len(cases))], height=speedups)\n",
+    "ax.set_xticks([i + i // 3 for i in range(len(cases))])\n",
+    "ax.set_xticklabels([name for name, case in cases], rotation=15, ha=\"right\")\n",
+    "ax.set_ylabel(\"speedup (total exetution time)\")\n",
+    "ax.set_xlabel(\"case\")\n",
+    "ax.grid(axis=\"y\", ls=\":\")\n",
+    "ax.axhline(1, color=\"k\", ls=\":\")\n",
+    "ax.axvline(3, color=\"k\", ls=\":\")\n",
+    "ax.axvline(7, color=\"k\", ls=\":\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfa18945-9ec8-4ceb-aabb-753a5d812b5a",
+   "metadata": {},
+   "source": [
+    "## Median timestep timings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "cf38da52-7cb1-48eb-a8ad-03a1af5a19dc",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_median_time_step_timings = df_stats.groupby(\"case_name\").median()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "92a36934-39e8-4886-913b-9c88c124868b",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_time_step_speedups = df_median_time_step_timings.copy(deep=True)\n",
+    "# Attention: relies on right order of the dataframe index\n",
+    "assert np.all(df_time_step_speedups.index == [name for name, case in cases])\n",
+    "\n",
+    "# normalize by iteration number\n",
+    "df_time_step_speedups[\"assembly_time\"] /= df_time_step_speedups[\"iteration_number\"]\n",
+    "df_time_step_speedups[\"linear_solver_time\"] /= df_time_step_speedups[\"iteration_number\"]\n",
+    "df_time_step_speedups[\"dirichlet_time\"] /= df_time_step_speedups[\"iteration_number\"]\n",
+    "\n",
+    "for col in df_time_step_speedups.columns:\n",
+    "    c = df_time_step_speedups[col].values\n",
+    "    # c[:3] = np.max(c[:3]) / c[:3]\n",
+    "    # c[3:] = np.max(c[3:]) / c[3:]\n",
+    "    c[:3] = c[0] / c[:3]\n",
+    "    c[3:6] = c[3] / c[3:6]\n",
+    "    c[6:] = c[6] / c[6:]\n",
+    "    assert len(c) == 9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "3513877d-2ac5-4cb1-b782-c1cc3e762eb4",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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K+f30/j2Nx8n2V7YrrLBCCim+Ka7V01drzXVr/NeQ53m64oorlJeXp1//+tf+8vXr1ysvL0+HHHJI2mtrxowZysvL0+233+7nsWnTJuXl5SkvL88/9ixZqv5zY99XP13t52ElLL/vrYTl51H9dOO6H/75Q4UU8pen1nXrXYWb/jtQxwjXdfX8888rLy9PJ554on/sOY6jc845R3l5eXriiSf8PF555RXl5eVp5MiRSiaTexwjGqoatHr6ar39/bdlyVJMjcWa9+9rXHfb4m2yZSuqqBJbGo+pNd9d4x97EUX2OEb06tXLb6/rurr22muVl5en2267zV/+0Ucf+cdJQ0ODn8ecOXP2OEZ4uzw/j61/36rV01dr86ObJcnPY/XVjes6tY6f30cLP2pc96HNezVGAAAAHOgOuKLDvHnzVFhY6P8UFRWl/XtFRYUqKir832trayVJZWVlqqys9Jff8I8bNGLBCN3z13v08LKHJUnvfvCuLnr0Io1YMEKP/uNR1cYbH/tCyQua+IeJGrFghFauXClJWrxhsYqLi/W5hz+nU35/iv/J286dO7Vw4UJJ8pdJUnV1tUpKSiQp7c1oVVWVlixZ4v+e+vRs7dq1Ki8v95e///77kqTzepyn46LHSZIK7UKNi42TJE3pOUV5dp4kaULOBA2JDJEkXZB7gf8cswpmqX+osUhwVOQof/ncXnOVb+WntauhocH/Iyb1B5Ik1dTUaNGiRf7vqcds2rRJpaWl/vINGzZIkiorK7Vq1SpJjX90nNfjPEnSpJxJGhAeIEk6NnKsTss5TZL0pdwv+c9xSc9LNCo6SpI0PDrcXz49f7oGhQeppYULF6q+vt7/3XVdOY6j4uLiVn8A1NfX+/2Uar+U3k9S43EjSd/80zf1UvlLkqRl7y/Tz576mUYsGKHZT8xW/e7Gbf7PC/+jOU/M0YgFI/T0m09LkkreL9HPnvqZrn78ao1YMEI1O2okSZc9c5nu+es9mvLYFH9bqf28aNEi1dTU+MtTx1FxcbESiYSkxuPacZy0fpKk5cuXS2rsp2XLlkmSolZU0/OnS5KGRIbosNBhkqQjwkdoSs/G7Q+LDvOf47Sc0/x+OiR0iL/8vB7n+f0kyf+DfsmSJaqqqvKXpwqBJSUl2rZtm6TGvkjltHDhQj/Xt99+Ww0NDXIcRy+88IL/HKmc+th9NCY2RpKUb+drVsEsSdLA0EB/3c+GP6tpedMkSX1Dff3l42LjdFHuRWppUs4kTcqZ5P+eaYzYuHGjJKm0tNTPaevWraqurpbU2E+pQkNpaal/7H3SGDHjmRl68p9PasSCEfpF6S8kSa9uelUXPXqRHvzbgxqxYITer218vU97dpq++adv6q6n79KsRY25f7DtA0mZx4jy8nJ98EHjOrt37/ZzWrJkiRoaGiRJa9as8Y/5FStW+M+xaNGiPY4RftFHlvKtfM3tNVeSFLM+PlOkf6i/309S4/EnNR57qX6S2h4jUu2XGsfy1PHz4Ycfau3atWn/LklvvPGGf+yl+kvKPEZcX3i9+th90toWU0xze831CxIpfew+ur7wev/3odGhkjKPEVVVVXrvvfckNY7jzfcrAADAgcwyqXfF+wHLsvTUU09p8uTJGdeJx+OKx+P+73V1dSoqKlJVVZX69+/vf+qVOmU4HA4rNzdXjuPIsiw1NDTopD+eJEUkz/YUUcT/hDNqReVFPXnyGj+piyfkyVNOJEdJOykjo6gTVdyNy1hGPaI9FFdcliyF42HFFdeKb66QMUaRSESJREK7du1SLBZTLBaT53kKh8PasWOHXNdVXl6eLMvy27xr1y5ZlqX8/Hz/U73UKeCp5zh+wfGNn7QlHUWsiBSVXLmNeSQcucZVNByVF2rMI+JFGj85l6ecWI4cOY3LExElTEIKSTnhHCWU0LKvL9OOHTsUiUT8fRaJRLR7927F43H17NlToVBIrusqFApp586d8jxPhYWF8jxPnufJdV3/9OLc3Fz/09hkMqkT/3iiwrGwHDkKKywv6cnxHIVDYVlhS65chU1YTqKxjdFoVMYycuUq5DRu1wt5ygk35uEaV+FEWK5clV9ZLsdxFA6HlUwmtWvXLuXk5CgWi/nLd+7cKcdxVFBQIMuy5DiOjDGKx+OyLEt5eXl+HvF43D8NPRqNatSCUbIcSwk3oZAVUjgaVlJJhRSSiRs5atxXlm3JkfNxey1PsWhMnrzGPOIhuXJlIkZROypXrpZ+fal27typcDisnj17KplMKhQK+cd6Xl6en1fqNeB5nvLz8yU1Fn5Sx3AoFFKPHj3keZ4SiYRc19WJfzxR0VhUCSVky5aVsJQ0jZcVhEIhJZWU7dlSUo15xCKy1JiHnbBljJEX8hQJR2Rk5HiOQsmQPHla+d8r5TiObNtWIpFQPB5P2++p9iaTSRUUFMi2bSWTSf+4kOSf0p86NV2SYrGYRj8yuvG1lQwr7sVlh2xFw415WMaSnbAb+yAaUthq7A/LsWS5lhy7sT9sNa6jeOMfyl7Ua3zdSHr90tczjhGO46hnz54Kh8P+vk0dL7m5uX4eqUsYwuGwotGoLMvSrl27dMIfTsg4RtgRWzE71rjctWQ7tpJWUuFo4xkYCSWkeGORzo24Ctth2bKVcBOyHEshK6TlVy7POEY0f815nqfc3FyFQiH/8gxJCoVCikajsm1b8Xjcf81GIhGNfWRsxjHC8zxFkhEllFAoFlJUUcUVl0kYRUxEyVBSoXDjmTJxLy4raSmkkNyYK1u2Qgrp1UtezThGuK6bNs7E43EZY2TbtqLRaOPxmkxq9+7dfg6hUEi2bWvXrl0a94dxGccIEzXKsRrzcB1XUTeqpJ2UFWk8oyGuuEzcKKKIkpFk4zYV1W5nt2zXVtgOq+xbZRnHiObLUsd97969VVtbq4KCgs5MjVDjXF9YWNih/VkxeEhg7Rnyn4pPXqkTaHvbaHtmtL1ttD0z2t62jrR9b+amA1W3/06Hjkq9sW4p9Ydk6lra1BvSlNT1yz179pQX8/zlSSWlcOM1wI4+/jQ8oYQUa3zjn1Di4+XhROO18LIUV+MbeCOjZKzxtOTUdiQpGo2mtSF13W9eXl5a21PLmx+sqTx69OiRdk22I6fxOuSYLVdueh7RxvY2zyNpJ9vMIxlN+p/qpfIIh8Pq1auXv07quvyWbUi1N/VHb2pZ6o+v5v0TCoU+7ouY/LY5cqRIY7s8fdwfjuX47W2ehxt2G/upWR6WZcmNuX6cam/L/Z5a3nK/N1+/rTyac+X6x4mRadzfqeVN7W3eH83bm1pX0sftleUvD4fDKiws/MT9nlqe+n/z5S2X2bb98fcJxOTvM0+ef5x4Tf9JjQW4tvLwop7f3lR/WLaV9hpK7aucnJy07zBILW+5L9tqr9S636Sm11bk41P+U3kYy/h/yDbvDxM2MmHj5+HnEmt8ruZ5tNxe8zGiuXA43Oq43lMeubm5smKZxwjp49ecCRm5IdfvD/81GmvsK0uWn4cVsqSQ/JwyjRFpr7k28mip5ZiaOibaGiMs25ITc/z97i+PNu5XS5afh2VbUuzj9qaOt5bby9TeVFGipT3t95ZjXdoY0aw/7LAtJ+y0zi/2cR6p/OywLYU/HrsyjREtvz8jVRQCAAA40B1wl1cAAAAAAIDuoduf6bBjxw698847/u+VlZVavny5PvOZz+jwww/vwpYBAAAAAIA96fZFhzfffFMTJkzwf589e7YkaerUqXrooYe6qFUAAAAAAOCTdPuiw+mnn6796LsuAQAAAABAE77TAQAAAAAABIKiAwAAAAAACARFBwAAAAAAEAiKDgAAAAAAIBAUHQAAAAAAQCAoOgAAAAAAgEBQdAAAAAAAAIGg6AAAAAAAAAJB0QEAAAAAAASCogMAAAAAAAgERQcAAAAAABAIig4AAAAAACAQFB0AAAAAAEAgKDoAAAAAAIBAUHQAAAAAAACBoOgAAAAAAAACQdEBAAAAAAAEgqIDAAAAAAAIBEUHAAAAAAAQCIoOAAAAAAAgEBQdAAAAAABAICg6AAAAAACAQFB0AAAAAAAAgaDoAAAAAAAAAkHRAQAAAAAABIKiAwAAAAAACARFBwAAAAAAEAiKDgAAAAAAIBAUHQAAAAAAQCAoOgAAAAAAgEBQdAAAAAAAAIGg6AAAAAAAAAJB0QEAAAAAAASCogMAAAAAAAgERQcAAAAAABAIig4AAAAAACAQFB0AAAAAAEAgKDoAAAAAAIBAUHQAAAAAAACBoOgAAAAAAAACQdEBAAAAAAAEgqIDAAAAAAAIBEUHAAAAAAAQCIoOAAAAAAAgEBQdAAAAAABAIPaLosPdd9+tgQMHKicnR+PGjdOSJUu6ukkAACCDjs7bjz/+uAYPHqycnByNGDFCxcXF+6ilAAAgaN2+6PCnP/1Js2fP1s0336xly5Zp5MiROvvss7Vly5aubhoAAGiho/N2aWmpLr30Ul1xxRUqKyvT5MmTNXnyZK1atWoftxwAAASh2xcd5s+fryuvvFLTpk3T0KFDde+99yo3N1cPPvhgVzcNAAC00NF5+1e/+pW+8IUv6Prrr9eQIUN06623avTo0fr1r3+9j1sOAACCEO7qBuxJIpHQ0qVLNWfOHH+ZbduaOHGiXn311TYfE4/HFY/H/d9ra2slSdu3b5ckua4rSQqFQmmx4ziyLEuhUEj2bluePLlyFVFErlx58hRVVI6cVnFMMSWUkJFpFccVlyVLUUUVV1y1tbVyHEeRSESe58l1XT/2PE/hcDhj7LqujDF+3FYe1m5LRsZve/M8OptTqu3hcONhk8rDGJOVnLRbsmTJkaNw06GZijubU11dnZLJpMLhsCzL8uNUHp3NSbslW7aSSiqkUFrc2Zzq6urkOI5s25Zt20omk43HaVPc2ZzMbqOwwkooIVu2Qgq1mcfe5NSy7ZnivcnJ3e2mvbZs2Wl5dDanurq6jGNEZ3PydnsZx4hs5NSy7e0Z99qbk9ltOjzudSSnurq6Do977c0p1fZsjOVt5dSy7Xsa91JzkzGm7QlwP7U38/arr76q2bNnpy07++yz9fTTT2fcTjbm+jrbluV5slxXXiQiy3VleZ68aFSW47SK3VhMdiIhyxg/ljHyYjHZ8bhkWfKiUYXiwc/1dZYlGSO7qe3yPD/ubE5Bz/V1kmRZsh1HXtM2/LiTOQU919dLMrYtO5mUFwpJzeNO5hT0XF9vjEw4LDuRkLFtmVBIdjIpEwp1Oqcg5/pIJKL6pj4OxeONbW+eRydzCnKuD4fDqnfdNscI0zzey5yCnOtDoZB2uG6Hxr2O5BTkXG/btna4blbG8rZyYq7fO9266FBdXS3XddW3b9+05X379tV//vOfNh8zb9483XLLLa2WDxw4MIgmdlivq3p1dRP22v7c9sKrCru6CXuNtncN2t41Pm1tr6+vV2Hh/ptzS3szb2/evLnN9Tdv3pxxO919rlevXl3dgr23P7d9f34t0fauQdu7xqes7QfaXL83unXRYW/MmTMn7RMTz/O0bds29e7dW5ZlZXVbdXV1KioqUlVVlQoKCrL63EGj7V2DtncN2t41aHtmxhjV19frsMMOy/pzfxrsy7le4ljuKrS9a9D2fW9/bbdE2/eEuf5j3brocPDBBysUCunDDz9MW/7hhx+qX79+bT4mFospFoulLesVcNW+oKBgv3uRpdD2rkHbuwZt7xq0vW0H4qceezNv9+vXr0PrS10z10scy12FtncN2r7v7a/tlmh7JgfiXL83uvUXSUajUY0ZM0Yvvviiv8zzPL344osaP358F7YMAAC0tDfz9vjx49PWl6Tnn3+eeR4AgANEtz7TQZJmz56tqVOnauzYsTrhhBN01113aefOnZo2bVpXNw0AALTwSfP25Zdfrv79+2vevHmSpFmzZum0007TnXfeqXPPPVePPfaY3nzzTd13331dmQYAAMiSbl90uPjii7V161bddNNN2rx5s0aNGqVnn3221ZdOdYVYLKabb7651Sme+wPa3jVoe9eg7V2Dtn86fdK8vWHDBtn2xydannTSSXrkkUd044036oc//KGOOeYYPf300xo+fHhXpdDK/nw80PauQdu7xv7a9v213RJtR/tYhnt4AAAAAACAAHTr73QAAAAAAAD7L4oOAAAAAAAgEBQdAAAAAABAICg6ICs8z+vqJuwTzfMMKufU16zwdSvoShx/HeO6blc3YZ/4tIz1aNunpf+Z6/FpwfHXMcz1e4+iQxc5kF7kxhj/m8h37tzZxa0Jlm3bqqmpUU1NTdq3r2frxVlWVqZBgwaptrZWlmVl5TkPRJ+WQb/lOLEv804df5+WPzI6wxijUCgkSYrH413cmmDZtq3du3crkUh0dVP2GwfKfM9cz1y/rzHXB4+5vv2Y6zv5nFl7JrSL53nyPK/VJLM/vilJDYqWZemNN97QhAkT9PTTT3dtowJgjNGOHTskSYsXL9aYMWO0fPlySdK0adP09ttvp70p6YzPfOYzisfj+n//7//528bHUvsjNeivXr26K5sTOMuytGvXLj388MOSGvPevXv3Ptl2SUmJfvGLX/jHdnl5+T7Z7v6k+Ri4bt06nXrqqfrnP/95QL5RTh137777rg4++GAtXbpUkjRv3jw9+uijXdm0butAme+Z65nr9zXmeub67oS5PjtzPUWHfSj1KYFt21qxYoVuv/12/fnPf9b27dv3y0p3KBTStm3btHLlSt12220aO3aszjnnnK5uVla98847Ouuss3TvvfdKkk4//XR95jOf0fe//3316tVLr7/+eqf6LjWxpgauI444Qj/72c90zz336K233tovj4sgpfZHPB7Xueeeq1NOOUUffPBBF7cq+5555hlt27ZNkvTSSy/pG9/4hl577TXdeuutOvbYY1VSUpL1bW7evFn33XefNm7cKKnxjccNN9ygP//5zzr11FM1ceJEbdq0Kevb3Z+FQiElk0mtWbNG//d//6f+/fvr5JNPPqBetzt27NBnP/tZPfzww3JdV4MGDdIXv/hFXXHFFTr44IP1+9//Xscee2xXN7PbOZDme+Z65vp9jbmeub47Ya7P0lxvsE/F43Fz1VVXmZ49e5rJkyebww8/3Hzxi180TzzxRFc37RN5ntdq2fHHH29yc3PN1772NdPQ0NAFrQqW67rmC1/4grnwwgvNm2++aYwxZujQoca2bTN9+nR/vbb2zSd54IEHzG233dZq+fbt280ZZ5xhzjjjjL1v+AHs1ltvNb/97W/Nd77zHfPuu+92dXOyrry83FiWZf7whz+YZDJpjDHmuOOOMwUFBebII480xcXFgWz3gQceMLm5uebRRx/1lx100EEmFAqZKVOmmJqamkC2uz9p+Tp3HMf893//t7Esy0yaNMls2bKli1oWjFS+F154oRk+fLh5++23jTHGDB482FiWZS655JKubF63t7/O98z1zPXdAXM9c31XYa4PZq6n6BAg13VbLXvyySfN6NGjzcqVK40xxtTV1ZkjjzzSnHbaaebDDz/c101sF9d1W+XiOI4xxpg333yz1QG5N5Nyd7R7925jjDEvvfSSGT16tLn11luNMcY899xz5ktf+pI5++yzzfLly40xHc85Ho+biy++2IwfP96UlZUZYz7ep6lthsNh89RTTxlj2j6WDnRt5bxu3Tozfvx4Y1mWueWWW7qgVcFKvfG4+OKLzejRo82GDRvMxo0bjWVZxrIs87vf/S7Q7Z911llm8uTJZv369Wbx4sVm+PDhxrKswN787C88z0t7fTb3xhtvmKOPPtp84Qtf2MetCl5qDNy1a5fJyckxN910kzHGmGeeecZ85zvfMYcddph5//33u7KJ3caBMN8z1zPXdwXmeub67oK5Pti5nqJDAFzXTTto6+rqjDGNE83MmTPN3LlzjTHGPP7442bYsGFm6NCh5tlnn+2Stn6S5pNBeXm5+d///V/zwgsvpOV3ySWXmGOOOcasXbu2K5oYuFWrVpmLL77YTJgwwSxatMgYY8zKlSvNgAEDzLx588yOHTuMMe1/s5Ba7+WXXzZnnHGGmTFjhv9vqTc0DQ0N5pvf/KY59thjs5nKfqHloJ8aDI1pfA09/vjjJj8/39x///3GGGMSicQ+b2MQHMfx+7+6utpEIhHz05/+1P/3G264wRx++OFm06ZNWd9uyksvvWSKiorM//7v//rLJ0+ebEaPHm3ee++9rG53f9H8df3++++bu+++2/zrX//yJ+CdO3eaH/3oR6agoMBs3bq1q5oZqG3btpkbbrjBDBgwwCxZssQY05j3YYcdZq699lp/vQPlj9COOFDme+Z65vp9jbmeub47Ya4Pfq6n6BCgiooKc8kll5jvfve7/mB59tlnmylTppjJkyeb3r17m9tvv93s2rXLGGNMfX292bZtW1c2uU07d+40V1xxhSkoKDAnnnii6d27t7nwwgv90922bt1qIpGI+dnPfmbi8XgXtzZ7nn76aVNYWGguuOACc9xxx5loNGquueYa/9Sz7373u2bUqFHmX//6V6vHNn9B1tbWtvkJhzHG3HTTTebEE080f//739P+3fM88+9//9v07t3bPPnkk0Gk1+1t3LjR/Pd//7f52te+Zn7xi1+Y//znP8aYxsng61//ujnyyCP9dQ+kP3b+53/+xzzxxBNm3LhxZsSIEf6pvtXV1aZPnz5mzpw5Wd/mxo0bzaWXXmruvPNO07dvXzNhwgRTWlpqjDHmgw8+MKFQyPziF784YN707Y3rr7/eFBYWmrFjx5pjjjnGjBo1yn+T8t5775lBgwb5f1QcKJ9W/vvf/zYDBw40n//8582kSZOMZVnm29/+ttm+fbsxxpgHH3zQxGIxs2LFiq5taDdwIMz3zPXM9V2BuZ65vjthrg9urqfoEADP88wPf/hDk5OTY77xjW+YRx991K8cPvLII8ayLHPppZf6nWlM44v91ltvNa+88koXtbpRywF9586d5vrrrzdnnnmmPxF88MEHJjc31/zoRz/yr2u68cYbzSGHHHLAvPn86KOPzMiRI83cuXNNPB43VVVV5oorrjDHHnuseeSRR4wxjW8whg4dar71rW+ZdevWmWeeeabVJFFTU2P+67/+ywwfPtzft57n+QNVRUWFOffcc83FF19sdu7caYz5+M3Ie++9ZwYPHmz++Mc/7qu0u41f//rXprCw0Fx00UXmmmuuMRMnTjSjR4/2/33RokWmf//+5mc/+5kxpvUbvP3Rtm3bzMSJE01RUZGZP3+++cIXvmCi0ai5+uqrTW1trTHGmN/85jemZ8+eZvXq1f7jOnuadllZmenfv7/5yle+Yh577DHzjW98w1iWZX7wgx+Y6upqY0zjJDxgwAD/FOO6uroDttLfcgzcvXu3+elPf2pOOeUUs3jxYmNM4x9f+fn5Zvbs2f5r+Xe/+52xbdvvm/39zXEymTRnnHGGufzyy00ikTDl5eXmJz/5iQmHw6a4uNjPb/z48WbSpEnmgw8+MEuXLjXXXnvtAfF6bK/9db5nrm/EXN+1mOuZ67sKc32jfTnXU3TopLYOttLSUjNy5EhTUlLS6t/Wrl1rjj/+eHPuueeahoYGE4/Hzfbt2821115rPve5z5nXX399XzS7lbau5TTGmB07dpjHH3/cf3E9+eST5rjjjjN5eXnmmGOOMf/4xz/8dQcMGGAuu+wyf9Dcn73++uumf//+5sUXX/SXbdu2zZx88snmkksuMZWVlcaYxsHn+OOPN0ceeaTp2bOnufnmm1s91x//+EczevRo88tf/tIY07oyevfdd5tx48aZ+fPnp/3766+/bo499lizbNmy7CfYTbR1zK1evdpcfPHFaV+29otf/MJYlmXuuOMOY0zjF3DdeOONpl+/fv6nhft7xXnJkiXmyCOPNK+99poxpnFsueeee0w0GjXPP/+8v96YMWPMCSecYB566CFz6qmnmssuu6xT233ggQfM4MGD/TcdxhgzZ84cc8QRR6Rd3zlgwABz1llnmRkzZphYLGbmzZvXqe12N3u6lvOBBx4wb7zxhjHGmNdee82MHz/eFBYWmkgk4p+GvX37dnPmmWeak046aZ+1OUirVq0yBx98sFm4cGHa8lNOOcWcfvrp5oMPPjDGNJ5+npeXZ0aOHGls2zY/+MEPjDH7/xuxthwI8z1zfTrm+n2Duf5jzPVdi7k+3b6c6yk67KW2DtrUjn/yySdNQUGB2bZtm6msrDRPP/20ue+++8xvf/tbY0zjm5S+ffuaQYMGmcmTJ5v+/fubUaNGddmE0/yAeeedd8xDDz1kli5d6n/RTW1trfE8z9x4441m4MCBZt68eaahocEMGDDATJkyxT/1sq0Brbvo6BvgN954w/To0cO89dZbxpiPryW85557TG5urvnNb37jr1tRUWEef/zxtOsRm2+zurraXHPNNWbkyJFm48aNxpjGSbP5v8+cOdMcfvjh5qmnnjJvv/22+ec//2mGDRtmvvnNb/rXkR5omr9+UsdaSupNyIoVK8ypp55qBgwYYM4//3yTk5PjX1+3fPly89nPftZceOGF+67RAbr33ntNv379Wi0fM2aM+cIXvuAfO2+//bY566yzzODBg83VV1/d6e1effXV5vTTTzfGpF8ze/jhh5spU6b4n9q+/PLLZubMmWbChAlpf4AcCJqPDx999JH5wx/+YMrKyvxralNvdn/5y1+aww8/3Fx77bVmy5Yt5swzzzRnnHGG/xp95plnzCGHHGI2bNiw75P4BJ7ndWgc3LZtm8nLyzPPPPOMMcb4lwW88sorxrIs8+CDD/rLysrKzJ///OesX4PcXRwo8z1zfWvM9cFjrk/HXN91mOtb25dzPUWHvdDyy0Z+9atfmYULF/oH62uvvWbOOOMM07t3b3PMMceY8847zwwbNszk5eX5laHy8nLz2GOPmdtuu838+c9/7pI8mkt96VVBQYEZN26cKSoqMhdccIH/gtqwYYM5/vjjzUMPPeQ/5rTTTjMDBgwwd999d7f+VKt5f6VuA9Mew4cPN1deeWXaspdfftnk5eWZz3/+8/51cJm21dwLL7xgxo8fb77zne+kLU/tt7Vr15qrrrrKFBYWmjFjxpgjjjgi7YuFDlSpT/2uvvpqc//99/v943meWb9+vRkzZoyZPn262bp1q1m3bp0pLCw03/72t40xjQPjgw8+aB588MGuTKFNzU+rbc+6xjReU5eXl+cfV6k3tg899JDJyckxf/jDH/xl27dv97+wbk/21IbUvz3++OMmHA7741fqdnjXX3+96devX9prvvmt8jqS4/7iRz/6kcnPzzcnnniiOfbYY83JJ5/s7/MtW7aYU045xdx5553GmMYxc/LkycayLHPvvff6y7rjPmk5Z6VO9d/TuP3RRx+Zr33ta2bSpElpy1955RUTDofN8OHD2zzFvvmXpB0IDrT5nrm+Neb64DHXM9d3J8z1H9uXcz1Fh0744Q9/aAoKCszYsWNNUVGRGTt2rFm3bp0xxpjNmzeb//u//zNLlizxO+uHP/yhGThwYFc2OaPUqYOp67i2b99uLMsy3/3ud83u3bvNX/7yF3PooYf6p4P95z//MVOmTDHHH3+8ueuuu9Kq+d3R6tWrzZlnnmlOOeUU89xzzxljPvnawD//+c/Gsizz1FNP+S/c2267zVxwwQVmypQpZs2aNcaYxmvvUgNRS82/ofq2224zn/3sZ/192HxwSF2rV11dbUpKSvxrPg9kjz/+uCksLDTnnHOOmTFjhjnrrLPM8ccf7++z22+/3Rx33HFpb+4/85nPGMuyzEsvvdSFLd+z5v1aVlZmFixY4Pfvniapd99913zpS18y5557btryOXPmGMuyTFFRUbur6i2P7dT1iW1Zv369GTlypJkyZUpaDl/+8pdNLBYzX/ziF1tVtQ+E62pbeu6558zo0aPNX//6V2NM4xiem5vr3yJw1apVxrIsf4zcunWr+eY3v2m++MUvmm984xtpfdvy07zuYPPmzeaiiy4yEyZMMI899pgx5pNPU3700UfNkUceae666y5/2UMPPWS+973vmSOOOKLVp/XdeQ7orANlvmeub425PljM9emY67sWc31r+2qup+jQDi13biKRMPPnzzcnnnhi2rcZ9+7d21x99dVt3m4mHo+br371q+bGG28MvL2ZtHWKqOu6Jh6Pm9NPP92fTP/+97+bE088Me06r0QiYfr27WtOPfVUc80115j+/fubefPmmfr6+n2eR0ctXLjQ9O7d28ycOdMs+//t3WdAFMf7B/BnDyUEUcGCKL1aQAEj0hFEsReEYDeKxopBxBI1ir3Gn71h7yEqlmiwx5qosSC2oGLHhmClSLnv/8X9b+XkQCzAHT6fN8qVZfZud77D7OzM+fMKV0AyMzNx8uRJAMpPopCQEBgZGaFhw4bw9vZG9erVxdmpAWD79u0QBAHNmjVTuD8vN/nPFy5cQMuWLRWGCKakpGDy5MkwMjISK4fSRtnnmp6ejoCAAIUKbuHChRAEQVwea/78+ahQoQLevHmDrKwsTJw4EZMmTcLs2bNx586dYiv/p3j9+jU6duwIXV1dhIWF4ejRowrP555oLLeoqCjo6+tj+PDhuHDhAmJjY9GxY0ccO3as0Ot2597mjRs38Ntvv0EQBJw7d07p67Ozs7Fjxw4IgoDQ0FDs3r0bCxYsQKdOnXDo0CFxSHVpUNC9nG3btkVoaCgA4Ny5c/Dx8UG1atWwfPly5OTkICMjA05OTrCxscHPP/+M2rVro0uXLnmGWquiU6dOwcTEBB06dMDp06dx8+ZNhedz12nAu2PoxYsX+PXXX6GhoQFvb2+0aNEC3377bYnNPVQcSkPec9Zz1pcEznrOelXBWa+aWc+dDkrIJw4C8q4LLJVKkZWVhT/++AP//PMPAOD48ePw8vKCtrY2atSogfXr1wOQzfq6a9cuzJgxA5aWlnB0dMS1a9eKbT/eL7fcs2fP8qyz3aRJE4waNQo9evSArq4uxo4dKw7pkjc2Dh48iCFDhsDDw0O8X1XVSaVSDBw4EL6+vnmW+EpPT8dPP/2EJk2aFDgh1v79+zFy5EgMGTIkzz2sy5Ytg6WlJby8vPDzzz9/cBmxpUuXok6dOti0aRMOHDgAKysrVKpUCevWrfv0nVRh+fUCP3r0CObm5khISEBCQgIaNWqEatWqYdasWeJnePXqVdjY2MDc3BxGRkawsrIS1w1WZTk5ORg+fDgaNGiAa9eu5Tkm1q1bl6fRKT8/3759i6ioKBgZGcHMzAxaWlro06fPR/cq37p1Cx4eHrC1tUVQUBAEQUC3bt0KXAZr9erV8PLygomJCYyNjRWWb1PFYYSfIy0tDffu3VMYQhoSEoIxY8agf//+KF++PAYMGIDHjx8DeHe1JyEhAb1794aXl1eeIdGqfEVowoQJaNCgAYC8jd8BAwagSZMmBd5PvmvXLkyYMAHBwcEK2aHK+1xYpS3vOes560sCZz1nvSrirH9HFbKeOx3ec+jQIRgZGSEqKkp8LDk5GVFRUbh7967Y0yUfBrZkyRKYmpoiNDQU2dnZcHNzg5eXF27evIlnz55h/Pjx8PT0xKJFi4p1P+Tle/9AGTFiBCpUqIBatWqhV69eePr0KXJychAeHg5NTU20atVKYXjVrVu3MGLECJWcMKqwgoODYW1trfS+zPnz58PX11fpvUmFqfxDQ0Oxbds2jB8/Hi4uLtixYweAvBW3fFt3795F9+7dIQgCJBIJRowY8am7pdJy7396ejoWLlyII0eOiEMP5bMCt23bFjo6OujTp484aVRWVpY4rO327duIjIwUr4iog7dv38LR0VHhHuHcM8bb2tqK96rmNznd06dPceLEiU+68pCTk4POnTujcePGePr0KS5evIjp06dDEATx+CzovfIJ1dRdfsE6a9YsVKpUCfb29mjRooXYUBw4cCC0tLTg5uamsEyZ/F5k+efy9u1bhQa2KjdA5GbNmgVBEMRh4rkdOHAAFStWVGiUyeVXB6rDPhdGach7zvp3OOuLH2c9Z31J46x/R9Wznjsd3vPff/+hY8eO8PX1BQBERkaiTJkyMDU1hZmZGbp16ya+NjU1Fd7e3pg8ebJYyfzwww/Q09PDhAkTkJWVhZSUlGLrOZRKpYiPj4ebmxs2b96s8FxsbCwiIyPh7++PmJgYrF27Frq6uggPD0daWhpiYmLg6OiI8PBwcVupqakYNWoU3N3dcenSpWLZhy8p9z2WnTt3Ro0aNdCuXTv8+OOPWLBgAR4+fIhz586hcuXKYggW9ruSn4g9e/bEqlWr8OjRI7i5ueGnn35CZmamOORW2QkbFRWF0aNHf/aay6okvxmODx06BF1dXVhYWMDExERhiSE3NzdUrlwZe/fuzbMt+YRS6igzMxPjxo2Dnp4e5syZg7i4OIWJoNasWQMrK6vP/j35TeKTmJgIPT09hTogOzsbnTt3Rt26dfHixQul23t/W6p4r+KHSKVSvHnzBgYGBnnqwLNnz+L06dNo3Lgxtm3bhlWrVsHQ0BCdO3cGAJw/fx5GRkYYNWqU+J7s7GzMmjULnp6eeZZEVPV723N7+fIlgoODUbVqVbRv3x69e/fGihUrkJCQgMuXL8PLywtnz54F8OE6sDRdCVPXvOesV8RZX3w469/hrC85nPXKqXrWc6eDElFRUXBwcMCIESPQp08f7Nu3Dw8ePEBUVBTKlSuH4cOHIzs7G//++y+qVq2KXbt2AZD1kvXo0QOenp4YNmyY0t6koiJfRxWAwnrTAPDPP/+gbNmysLa2xqZNm8TH58+fDzs7O0RHRyMnJwe//vqrOJtrnz59YG5uDltbW/H+RVWW38khr0znzJkDFxcXHD16FBMnToSTkxOqVKkCd3d3GBsbIzIy8pNmYvXy8sKePXsAyIas1axZE+XKlYOTk1Oe18q3XZoa7YBsUh5jY2OFhkNsbCwGDRqEBQsWYM2aNcjKysK+fftQtWpVDB06FACwc+dOlCtXDtOmTcPNmzeRnJyMZcuWwdraGhEREeISPepq0KBBMDU1RcuWLWFvb4/mzZtj/PjxGDduHHr27FmoWYXzk/s9cXFxCjMLZ2ZmwtTUFEuWLAHwrjF84cIFaGpqYuHChZ+xV6rrxYsX4qRs8nNSLikpCYIgwNDQEP/73//Ex48dOwZBEMThpREREbCzs0OtWrUQFhaGunXrwtjYWC2WDfvQFYmoqCj4+flhy5YtGDZsGNzc3GBsbAxra2vo6+tj1apVSE1N/eCw8dJG3fKes56zvqRw1ivHWV+8OOvVN+u50yGX3MOdQkNDYWBgAG9vb4XGxIYNG6ClpYW4uDgAsqWWHBwcMHjwYNSuXRt9+vQp8J7BohAVFQVHR0fs379ffOzly5eIjo4W7+vq168ftLS0xNlaAVkg+vj4IDAwUOytP3jwICZNmoS+ffsiMjKyWPfjU+WulOUV0fuVe+fOncWJYwBZZZ2QkIB27dpBEATo6uqiSpUqOHjw4Ef9zsDAQNy+fRtPnjxBs2bNoKGhgVq1aok9paWt0SH34MEDnDx5Mt/KLyYmRqz4//vvP/HxFStWQFNTUxzSNmHCBNSpUwfGxsawt7dHtWrV1OKe14IqffnwymbNmomhd+DAAYwdOxYdOnRAuXLlIAgCevbsiY0bN37wKk9SUpLSIdTPnj1Dy5YtUaFCBRgbGyM4OBhPnjxBamoqevTogZYtWyI5OVl8/ZUrV1CuXDlYWVkpnfxOncXExMDW1lacDA+Q/RESExMj1glz5syBIAhiHZj7HLa3t8fr16+RlZWFixcvYvjw4RgwYACmTZum8HtU9WpH7nLl98dvv3790LJlS4XHEhMT0aVLFwiCAGtra5QrV67El3QsLuqY95z1nPXFjbOes16VcNard9Z/VZ0OHxMKhw4dgq2trbhuae6ZUGvVqiWuwXzhwgWMHDkSXl5eeQ7a4nLixAn4+vqiT58+4mNDhgxB7dq1xV67hIQEGBkZYcyYMQqNpD/++AO2traYN2+eyp5k+cldKZ85cwbe3t7YuHGjwmvk3/kvv/wCU1NThZM0MzMTXbt2xZo1a3D37l38+++/H/X7U1JSUKNGDfj7++Obb75BYGAg5s+fDycnpxI7FopLhw4dYGRkpDCz9NixY8V1tN+8eYOffvoJ1apVU5jxNzU1Fa6urmKFmJOTg/v37+PkyZPYvn17se7Dp3i/Djl//nyeIYzy88jZ2RlDhgzJsw13d3f4+vqiVatW4r2Y+Tl69Cjq16+Pw4cPKzx+4MABTJkyBf3798fVq1exadMmVKlSBaNHj0Z2dja2bNkCJycnhdnz9+3bh759+8LIyAgjR44EoB73KBbG69evYWlpidDQUPHzXLp0KQRBEGesT0tLg6WlpUI9Ccga1eXKlcOcOXMUPo/c9aGqDj/NXd6bN2/Cw8MD27ZtU7ofy5cvh7GxsbjMo1xoaCjGjRuHZ8+eFfoPMXVQGvOes56zvrhx1stw1qsGznr1zvqvqtNBrqArE7lnlp00aRIMDAzEA1l+MAYFBSEoKEihUirpE3rGjBlwdnYWZ9K+fv06PD09MWDAAPHEHDVqFOzs7HDgwAGF97Zr1w5OTk4Ky0upi+TkZMTFxaFt27YYNmyYQm9vbtevX0fVqlXRrFkzzJgxAzt27IC7uzu+++67T76HNTs7G/7+/qhfv7445BYAvv/+ezg7O5fYSiVFSX7MP3v2DNra2pg5c6Y4RKtHjx4wMDAQj7dLly5BV1cXs2bNAvDu3Dp8+DDKli37wYmOVFlmZiYCAgJQqVIlxMXFKV1mb+zYsbCyslKYHX/79u3w8/PLs+ZxfnJycvJMCCRf8srMzExhDfNJkybB3t4eMTExyMrKwsyZM1G2bFn4+fmha9eu0NPTw6ZNmzBhwgTY2NiobLh+LHndu3z5cpiZmSn03tepUwedOnUS64XffvsNZcqUEVcikH9vI0aMgCAIedZGl0qlKv8HWmZmJv777z8MHz4cHTt2xOPHj5X+wX3q1Cm4ubnB0dERS5cuxb59+9CmTRtYWVmJn4ecqu/zxyhtec9Zz1lfHDjrZTjrVQdnvfpn/VfV6XDgwAHUqVMHkyZNKnC9VfmXEBsbCz8/P3h6eorPpaeno27dugprDpck+QF39+5ddOjQAe3atRMnLZoxYwYaNGggrvmbkZGBOnXqoH///uLyMIAspOUNLVWm7ORwdHSEtrY2goKCPnhP7dGjR9G4cWM4OjrC3t4eAwcO/OzK+MGDB+LEQfLv4tq1a4UOGnUkr/jHjRsHfX19cfbplJQUVKtWDaNHjwYg+zwmTpyI8uXLi0MGAdn32Lp1a3Tq1Kn4C/8FjBkzBsuXL8fgwYPz9CTndvr0aXFpvWbNmqFt27b49ttvMXPmzA/+jvfXmL506ZLC2t1du3ZF5cqVceLECfGxtLQ0uLq6onv37uKs9Dt37sTQoUPRpk0b8f7vH374AU2bNkVGRobKh+zH8vLyQkBAgDikd//+/eJM3vLz09vbG35+fgr3M+bk5KhFw/j97ys7Oxt9+/aFIAho2rRpgVfSpFIpLl68CDc3N9jZ2aFu3bro3LmzWq9WUJDSlvec9Zz1xY2znrNeVXHWq2fWfxWdDllZWVi+fDlq164NGxsbGBsbi5XnhyxfvhxaWlqwt7fHzz//DHt7e1hZWRXbDM8fM0R07dq1cHFxwfTp0wHIhr01a9YMQUFBYk/8mjVrYGRkJA6NUwe5lyCSk1fSZ8+ehSAICqFWUOWak5ODlJQUhWBkHyf352tgYIC+ffuKVxMXLVqEb7/9VlxyKDExEbVq1ULfvn0V3pt7hmdVpezce/PmDVq0aAFBEDBo0KAPbuPNmzeYMWMGRo8ejVGjRin8AVAY8j+WevbsCUdHR7ExERsbixo1amDatGnifYwAsGnTJtjZ2WHx4sVKt3f8+HE4ODhgzpw5H1WOklSYxpK8Pjh8+DAMDQ2xaNEi8Q+T5s2bo2HDhrh//z4AWZ2hoaGBNWvWFF2hv7D3G6a5/fvvv7CyskLz5s0Lta23b98iLS1NYULCkh6p9yWpa95z1nPWqxrOes764sRZX/qz/qvodHjz5g0WLlyIKVOm4PXr16hRowb69u1bYGUoP/jv37+PH374AeXLl8fy5cvzPcG/tPdPvkuXLuU7TFT+2jdv3qB3797w8fERZ7jdvHkzHB0dMXv2bPH1bm5uWLp0aRGV/MvKHQZxcXFYsGABDh48qPB4x44dYW1tjRs3bhRqm6Wtx/dLKuxnI79qFBUVhbJlyyrcH2Zvb4/AwEDx540bN0IQBFy4cOGLlrWovF/pv78G9JkzZ6Cvr4+xY8cCyP+Phc+dWOyPP/5Ao0aNAMjuJXd1dUVYWJhYDwwaNAj169fPs7xTo0aN0Lx5czx8+BCA7KrIunXr0KlTJ2hpaWHo0KFqMemZVCr9pHJ269YNHh4e4jDCe/fuQSKRYMGCBeIVD/lkc+pQF+T+DB48eICFCxfi+PHj4tJ/qampGD9+PMqXL1/g1Q9lPvUzVmXqlvec9TKc9cWLs56zXlVw1st8DVn/VXQ6ALKD8fXr1wCArVu3okyZMnnud8zPpk2bMHny5BJZ1mf37t2oV68ebG1tYWlpiVGjRomzVOcmP5j27dsHLy8vhUltevToATc3N3GCGnVbnig1NRW9e/dGhQoV4OLigsqVK6NDhw64desWANns42XKlMGMGTO+uuXevqTcFdLH9NI7OzvDz89PfM/+/ftRtmxZ7Nu3D4BseaPx48d/dM9/SXv48CF69uyJZs2aYdiwYTh37hwA2fkzfPhwVK5c+YucS+/fSyj//9GjR6GhoSEutxUREQFnZ2dxTerk5GRYWVkhPDxcYdhcfHy8Qs82AOzduxcRERG4efPmZ5e3OOT+PM6cOYPRo0eLw8LzWzNbfvzeuXMHJiYmGDdunHh/Z3h4OPT09MQr3up4j+vw4cNRsWJFNGjQANbW1nBwcFAYcm9hYSFekVOFxkVJUse856znrC8unPWKOOtLDmd9XqU567+aTgc5+Rfk6uqKpk2bFlg5luRay+np6Rg3bhyMjIwwa9YsnDp1CnPnzoUgCOIs1fn13I0cORJubm7Yu3cvAOCvv/5CmzZtxGFwquz9fUpNTcXw4cPh6+srLseUmJgIbW1tjB8/Xuzt++WXX6Cvr6+whjH7eOfOnUPTpk0xYMAAXL58GUD+w7Hkj1+8eBGCIIhrdANAq1atYGJiUuC91KosMjISlSpVwvfff48JEybA398fhoaG4tXSW7duwcjISFyH/FPriNzve7/n+uzZs3BycsIff/wBQDahl4+PD7p37y5OWDV//nxUr15dbJy8v235+aQOvfzvS09PR8+ePaGjo4OQkBBER0crfF7yYy33vsmfj4iIgK2trdgYBgBzc/M8kyiV9FBDZd7/rtLT0zF16lR4eHjgyJEjAGRLq1WoUEHhStbq1ashkUjEe1zV8Tv/0tQh7znrZTjrixdnvQxnfcnjrJf5GrL+q+t0kB94ly5dEitP+RepSidtQkICXFxcEB0dDeBdmZo3b44ePXoofY98P+Li4tCsWTO0bNlSba4GKLuXE5ANd9uyZYt4ckVHR6NevXrQ0dGBtbW12CgDACMjI3Tv3r1Y100vTZYsWQIdHR2Ehobi77//Fu+LA2SVYe6f5eTfWY8ePVC3bl1x2GtCQgKWLVtWPAX/DMqOubt372LAgAHi7PCA7P5oQRAQHh4uPrZkyRJoaWmJE0x9TL2R+7XPnz9HZGQkXFxcFIZGJyYmonr16tizZ4/42OrVq1G/fn0sWrRIfKxx48bYv39/oX+3uti0aRNq164tLm+X+zPbvn07QkJC8gyZl78mKysL9erVQ1BQkNhoU4V6vSAF3cu5cuVK8XM4deoUXF1dUbFiRZQtW1a8qv38+XP4+vrCzc2t2Mqs6tQh7znrZTjriw9nvQxnvWrgrH+ntGf9V9fpALxriAQHB8PGxga3bt3C1atXMX/+fIWJWkramjVrxCFd8pOoQ4cOCAsLU3hMmenTp2PixIl4+/atWpyAcjdv3sSaNWtw7tw5sXfz5cuXkEql+OWXX2BmZoZp06YhIyMDRkZG6NKlizj0cuXKlahVq5ZKzNCqblJTU9GoUSPx3sXc30lSUhIcHR0Vwk9OHuRv3ryBIAgYM2aM2gxny13pvz8b+p9//gkAuHLlCjw9PWFkZISePXuiTJky4izSycnJcHBwgIuLyyeXYdWqVTAzM8PYsWOxcOFCaGtrY+rUqeKM1D4+PggJCVF4z/fffw8PDw+cOXMGgPoNryuskJAQ1KhRQ+lzY8eOhbOzM169eqV0lmcAWLFiBXr16iUOs8/9nKrJvQ/JycnYsGEDLly4IB4H8snw5s6dCxMTE4SFheHp06fw9fVF48aNxXuRY2JioK+vn2c5sK+ZOuQ9Zz1nfXHhrOesVzWc9V9P1n+VnQ7yEzczMxM6OjpwdHSEIAjo0qVLnolkVIX83s66detiwYIF+b5Ovm/qEgZy2dnZCAkJQYUKFeDs7AxjY2O0a9dOPKHu3bsHR0dHhVloGzVqBCMjIyxatEjlG1uq7vnz57C1tUVQUJDS51u0aIGuXbsCyBt88mPt999/V7v1358/f45BgwahU6dOmDJlitjDDMiGQLq5ueHHH3/Ew4cP8fbtW1hYWCAoKEj8A+HgwYOIjIz86N979uxZLFu2DJ06dcLWrVvFq5SLFy+Gl5cXGjdujKdPn2LgwIH48ccfkZaWJobo3r17MXDgQIUGd2lsjMTGxqJq1apo164d1q1bh7///lv8I/H+/fvQ1tYW/wgpLee/fJIoFxcX2NjYwN3dXRy2/PTpU3h4eIhXyLKzs9G+fXsIgiBOFpidnV0qj4XPoW55z1nPWV+UOOs561UNZ/3Xk/VfZacDALx+/RqbNm2CpqYmHB0dxXsiVdmtW7dQvXp1pUPf1N3q1avh6OgoTv7y/PlzCIKAIUOGID09Hdu2bUP16tVx6tQpAMB///2HLl26wNHREXPnzs13whkm86HKSSqV4vjx4zAxMYGDgwMGDBiABQsWiPeVbdy4EU2aNBGvEqhjZfe+3bt3o3LlymjdujXGjh2LwMBA6Ovr48mTJwBkveeWlpbikMorV67AxMQEgiBg7dq1hf49yj4rX19f6OnpoX379gCg8LneuXMHVlZWGDBgAOrWrYt27drlux119DHn6fbt22FmZoaBAwfC0dERlpaWaNOmDUaOHImmTZuKE04VRFWveLxv3759qF+/Pnbu3AlANsGbtra2uETg5cuXIQiCWEcmJSUhODgYLVu2RM+ePZXeA8tk1C3vOes56z8VZ31enPUlg7Neua8567/aToedO3dCQ0MDM2bMKOmiFNqGDRtQv3598eekpCScOHFCbU40Zfcx5eTk4O3bt/D29hZ78P744w+4uLjA1NRUHPqWmZmJatWqwdPTE4MHD4ahoSGmTZumMJyKFaygidLkj/3444/o1q0blixZgqZNm8LU1BTW1tawsbFB27ZtERcXV6xl/hLym/24V69emDhxovjY1q1bIQgCfv31V/FnTU1NXLt2DWlpaZg+fTqmT5+OZcuWib3uBSnovDx27BjMzc3h5+enUKbc92qPHj0aEokEurq6SofQqXvD5P3vRdn39MsvvyAgIACAbIbxAwcOYOzYsXBycoIgCKhVqxYGDRqEv/76qziK/NkKupezbdu2CA0NBSCb5M3HxwfVqlXD8uXLkZOTg4yMDDg5OcHGxgY///wzateujS5duqjtBG7FSd3ynrOes/5zcNa/w1lf8jjrFX3NWf/Vdjq8ePFC6XJUqkh+4Pbq1Qv9+vUDAEydOhWCIGDEiBFqsR+5K5lnz57lWWe7SZMmGDVqFHr06AFdXV2MHTtWnDhG3tg4ePAghgwZAg8PD6xYsaL4Cl8KnD9/HmZmZnjx4kW+r8nMzISRkZHChF0PHjzA4cOHUaFCBQiCgEqVKsHLy6tQQawK8usFfvv2LUxMTPDvv//i/v378PT0RLVq1TBz5kxx+OPr16/h7OyM6tWrw8DAAMbGxuI9nh9jyZIl6N+/PyZMmKCwvNXgwYPh5OQkTgylLJgnT54MQ0NDcQmv0uD06dMYM2aMOAnc6dOn87xG/lmMHTsWlpaWeZ4fNmwYmjZtipkzZ6Jp06biFSp1kZaWhnv37incXxwSEoIxY8agf//+KF++PAYMGCCutiDPgISEBPTu3RteXl6YOnWqwjbV5Q/SkqAuec9Zz1n/uTjrFXHWlxzOes769321nQ7qJjMzE05OTvj+++9Rs2ZNmJqaKgSGqpFPhvL+yTFixAhUqFABtWrVQq9evfD06VPk5OQgPDwcmpqaaNWqlTihCiAbZjpixAieMOoz3b59GwYGBuLavspC7+3bt/Dz8xOHeMk9evQILVu2xK5duxATE6NwL6Sqyn1lID09HTNnzsS2bdtw9+5dALLjqlmzZvDy8oKOjg769OkjDmXOzs7G33//DUDWaN65c6fC7Nb5ef8zPX/+PL777juYmZlhyJAhMDU1hb+/P2JiYgDIhnA6OzsjJCREDOX3Z9TPyclBhQoVcOjQIaW/Qx0kJSVh5syZ4jJ+GzduhK6uLmbPng0PDw8IgiB+L+87fvw4dHR0MH/+fCQlJSEjIwN79+6Fu7s71q1bV5y78dHymy9g1qxZqFSpEuzt7dGiRQux4Ttw4EBoaWnBzc1NnMEfkA0/DwsLEz+/t2/fKjSw1bkBwvLirOes/xyc9Zz1JYWzXhFnfV7c6aAmrl27BkEQUKVKFcycObOki6OUVCpFfHw83Nzc8qwlHBsbi8jISLEiXrt2LXR1dREeHo60tDTExMTA0dFRXKZIKpUiNTUVo0aNgru7Oy5dulQSu6S25IGVu5Jat24dJBKJuCb3+6GWk5ODefPmQU9PDxMmTMC+fftw8OBBNGjQAG3btkVSUlLx7cBHioqKyrP2NSAb2mhgYAAbGxtYWFjAyspKbNR27twZ1apVE49V+edx6NAhdOjQAQ8fPvzk8mRmZmLAgAHo27evQlmMjIwQFBSE58+fAwAmT54MZ2dnREVFKd3Oq1evUL9+ffz++++fXJaStmPHDhgbGyv01pubm0MQBAQGBhY4bPrVq1eYMmUKypYtCzMzM/j6+kJbWxsREREKr1OVMJZKpXjz5g0MDAzy1IFnz57F6dOn0bhxY2zbtg2rVq2CoaEhOnfuDEDWcDUyMsKoUaPE92RnZ2PWrFnw9PTEsWPHFLbH97aXTpz1nPUfg7NehrO+5HHWy3DW5487HdTIwoULVfaentxDyeQ9tXL//PMPypYtC2tra2zatEl8fP78+bCzs0N0dDRycnLw66+/irO59unTB+bm5rC1tRUnlGKFs3LlSkyePDnP48+fP0fjxo3RuHHjPM/JKzR5T3WlSpVQu3ZtWFhYYMiQIUVe5s9x5coV1KhRQ6EH/cqVK+jVqxemT5+OyMhISKVSXLt2DdbW1uLVnWPHjsHCwgK9e/fGpUuX8ODBA0RGRsLKygpDhw7Nsy50YSxevBg7duwAIOu5l68bPWLECFSsWBHfffcd7O3tMW/ePACyYd9NmjRBixYtcOfOnTzbCwoKgq6ubp4hyuqmb9++aNKkCU6dOoW4uDg0adIEVatWxaJFi8RGREGhevDgQWzatAnz588XJ//60HuK24sXL8QZt3Ovtw7IzitBEGBoaIj//e9/4uPHjh2DIAiIjo4GAERERMDOzg61atVCWFgY6tatC2NjY5W+0s2+PM56Vhic9Zz1qoaznrO+INzpwD5bVFQUHB0dxfvVANl629HR0eI9qP369YOWlpY4Wysg673z8fFBYGCgGCIHDx7EpEmT0Ldv309anuhr9/btW3Tq1Amurq64cOECAMWe4b/++gtlypQRK778eo1fvHiB+Ph4lR3q+ujRI+zcuTNPY0G+P/Hx8RAEATo6Ogr3Ee7fvx+CIIiTlq1duxYNGzaEgYEB6tati+rVqxdqtmplEzvdunULdnZ2WLJkiViWzMxM9OzZEy4uLvjrr7+QnZ2NevXqoVGjRuKSY8uXL8eoUaPEEMtt1qxZYmNG3eQ+tk6ePAkPDw+Eh4eL9zYOGjQIDRo0KPDe2fwm0MrOzlapRkhMTAxsbW3F4wqQ3WMcExMjfq9z5syBIAhiHSgvf2BgIOzt7fH69WtkZWXh4sWLGD58OAYMGIBp06Yp/B5V2mf29eGsVx2c9Zz1qoKznrO+sLjTgX22EydOwNfXF3369BEfGzJkCGrXri322iUkJMDIyEhhUhlANnu1ra0t5s2bV2pPsqJQ0KzUR48eRePGjcV7OoF3FVhGRgaCg4NhY2NTPAUtIiEhITAxMVEYhhYREYGIiAix4p82bRrKlSuH//77D8C7z6B169ZwcnISGzHPnz/H1atXsW/fvk8qS+7j1tnZGcOGDRN/vnz5MoyMjBS27e7ujipVqigMxyytHj58iMDAQISHh8PGxgZ2dnbYvn07AODJkyewsLDA0KFDkZycXOhtqmI98fr1a1haWiI0NFQc+rt06VIIgiAu9ZWWlgZLS0uFehKQTeBWrlw5zJkzR6Hxlns/1W1ZLFY6cdYXP856znp1wFnPWV8YEmLsM7m7u5Ofnx9dunSJNmzYQEREAwcOpCpVqtCePXsoKSmJLCwsqHv37rRz5046c+aM+N7WrVuTlZUVbdiwgW7evFlSu6B2JBIJvXr1imJjY4mIKCcnhyQS2ens5eVF7u7udO7cOdq9ezcREUmlUiIi0tTUpODgYEpOTqbt27eXSNk/h3w/Jk2aROXKlaPt27dTUlISERFlZWXRrFmz6Nq1a0REFBISQnp6erRs2TIiIhIEgYiI5s+fT3FxcbRixQqSSqWkq6tLtWvXJj8/P4XfBSDfcmRlZVH//v1p27Zt4naJZMfziRMnKDs7m4iIEhISqFKlSvTixQsiItq/fz+ZmJhQnz59qEOHDgr7JP+3tIiPjydvb2/KyckhHx8fCgwMpISEBNq6dSvdv3+f9PX1qX///vTnn3/SsWPHiIjo5cuX9PjxYyLK//PP/XmrgpycHNLR0aGff/6Zdu7cSUeOHCEion79+lHt2rVpwYIFlJKSQt9++y1NmTKF1qxZQ6dOnSIi2T4aGhrSoEGDaOjQofTw4UNxu4IgEGQXBqhMmTIlsWuMKeCsL36c9Zz1qo6znrO+0Eqqt4OVDvIe97t376JDhw5o166deB/WjBkz0KBBA6xevRqArOe9Tp066N+/v7g8DABcv35d7CFkhfP8+XN069YNdnZ2CrMgy7+Pa9euoVWrVujYsaN4NUDes3rnzh3UqlULGzduLJnCfyb5fsyfPx+WlpbYtm2b+Jy5uTl69OghTt60evVqfPPNNzh//jyAdz3K/fr1Q+vWrcXZhHMrTO/6q1evMHDgQBgYGGDy5Mnie+bMmYNGjRqJs2PLZwM3NDSEj48PNDU1sXz5cpXswf/Sdu3aBUNDQ4X7V+fOnaswLBUAvLy84OzsjN69e0MQBHGCOXXk5eWFgIAAcUZq+RDfHTt2iOemt7c3/Pz8FI69nJwc8f5gxlQRZ33J4KznrFd1nPWc9YXFnQ6sQPndZ6XM2rVr4eLigunTpwOQLSPTrFkzBAUF4dq1awCANWvWwMjICKtWrSqS8n5NNm7ciPr162POnDkA8n5XixYtgrOzsziZjfz506dPw8bGRgxndZM7xF1cXNCxY0fEx8cDkIWfRCLBnj17xNe5urqiffv2CmvcK2sI5G7IybfVu3dv3Lt3L9+yzJgxA/b29ujVqxcA2RDLMmXKKMyGHR8fj5UrVyIsLEwsZ35lKE2mTp2K+vXrIy0tTWGGdQ8PDzRu3Ficpf78+fOYPHkyWrdurXCfpKoozPckbxwfPnwYhoaGWLRokXg/a/PmzdGwYUOxcXr27FloaGhgzZo1RVdoxj4SZ73q4qznrFdlnPWc9YXFnQ5MqfdPvqNHj+Lq1asF9ha/efMGvXv3ho+PDy5evAgA2Lx5MxwdHTF79mzx9W5ubli6dGkRlr50yK8ClD/+7NkzDB48GPb29mLw5V5a59mzZxg0aBBMTEywfft2XL9+HXv27IGtrS2Cg4PzXVu4JBU2nOUVf0xMDAwNDREZGSneC9eoUSN4enqKs6yfPHlSYdZgZXI3QC5cuABHR0dxcir5lRRlr8/MzMSRI0dQuXJl9OrVCydOnICPjw8WLVpUYNnVvRFSUPnlz/3zzz8QBEFscMi/H/nM9ePHj8+zVrn8/x/zB1BR+dRydOvWDR4eHvjnn38AAPfu3YNEIsGCBQvE+rNz584IDQ1V++OAqT/O+pLHWZ8/zvqSxVmfP876j8edDqxAu3fvho2NDWrVqgUDAwMEBgbi5s2beV4nP2H37dsHLy8vhaWXevToATc3Nxw+fBiAbJIVlr/clZ/8//nNPH3w4EG4urrip59+UnhcXsHduHEDAwYMEJdwMjU1VVhDWZXk3u/cS2J9yPfffw8fHx/8+++/AGRXG8qUKYPIyEjxisfcuXMVlnpT5tWrV+jUqRM0NDTw008/4cyZM6hSpYo4gVV+V0wA2SRpgYGBqFq1Kuzs7DB37twP7qM6ev843LNnT74TQ2VmZorDC3Pvd9++fVGjRg00btxY4WqQsu2XlNzf9ZkzZzB69GhxWHh+a2bL9/HOnTswMTHBuHHjxM8mPDwcenp6iI2NBVD6J4ti6oezvvhx1nPWqyrOes76osCdDkyp1NRUjBw5EtWrV8fs2bNx7949HDhwAOXKlcPvv/9e4HtHjhwJNzc37N27F4Bs6aY2bdrgypUrxVF0tSWVShUquPHjx6N9+/b5vhaQ3Ts7adIk1KxZU1zjPHelL7/n9tmzZzh27JjSpZpUyblz5+Dj44Pvv/9ebFjkF065l8wyMjLCpEmT8OLFCwBAr169oK2tXeglqNLS0mBtbQ1fX19xBuy4uDhYWFiI9ykrk/v7Sk5Ohq+vLwRBKJVLwOXe1/j4eOzevRuCIGDLli35NrAuXrwITU1NBAUFYfXq1Zg3bx6aN2+OP/74Aw8ePCiuon+S9PR09OzZEzo6OggJCUF0dLTCfsobE7k/F/nzERERsLW1VZjJ3NzcXLwiIqcqDS/29eKsL36c9Zz1qoyznrO+qHCnA1MqMTER48ePF69YyE+2+vXrK0zmk5v8JIyLi0OzZs3QsmVLpUM0WcF27NiBGjVqwMzMDHv27Mn3dfLv5MKFC2jZsiX8/f3F51JSUjB58mQYGRnht99+K/IyfwkbNmxA+fLlERYWhjNnzihMSpSeni5O2KOs4h85ciTq168vrh+flZWF5cuXf9Tvz/375KpXr46ZM2cCKDg0cg9zvXXr1kf9XnVy9+5deHh4iMtBCYIAPz8/pffCyj+T3bt3o2PHjrC2toa5uTm2bt0qvkaVrwZt2rQJtWvXFhvEuY+77du3IyQkJM/a8fLXZGVloV69eggKChIbwzy8kqkizvqSw1nPWa+qOOtlOOu/LO50YPm6ffu2QuU7c+ZMGBoaYuzYsdi3b594EiqrTKZPn46JEyfi7du3fAIW0uvXr9GzZ08IgoDFixcrfU1+n+XSpUtRp04dbNq0CQcOHICVlRUqVaqEdevWFWWRv5i3b9+ibdu2edY1BmSzd7dq1QpDhgzJM1RNfuylpaWhatWq+PHHH8UrIJ9Dvt2OHTuiVatWn/T+0njcDxgwAK6urkhJScGtW7fw22+/QSKRYO7cuQqTdynzfgNN1T+fkJAQ1KhRQ+lzY8eOhbOzM169epVnP+R15ooVK9CrVy+8fv06z3OMqRLO+uLFWc9Zr+o462U4678s7nRgH5SamoomTZqgUqVKCA8Ph7+/P+rUqYPQ0NA8r5VX4Hwf08e7ffs22rRpAxcXF/GxxYsXY8OGDWKv/vvkleDdu3fRvXt3CIIAiUSCESNGFEuZvyRPT094e3srvR+zX79+4vDT9xu+8sp97969Su9B/hzBwcFo2bKlQpiUdsomv8rJyUFKSgosLS3FGdLlhgwZAiMjI1y+fFnp9t7flrrUDbGxsahatSratWuHdevW4e+//xaHLN+/fx/a2tpi40rVG1WMFQZnffHgrOesVwWc9TKc9cWHOx1Yofz9998KM/uOGTMG7u7upXp4WUmIjo5G/fr10aJFC9SqVQvOzs6oU6cOBEHAqFGjxHvjlFV8UVFRGD16tHhvpyopzNC6mzdvwsnJCVZWVujatSvGjRuHrVu3Ij09HTExMahVq5bYICjqoXry7UdGRkJPT69If5cqyX1cnT17FocOHVIYNm1jY4OIiAgAEB9/8eIFypcvj5EjR+Lly5fFWt5P8TGNhu3bt8PMzAwDBw6Eo6MjLC0t0aZNG4wcORJNmzYVJ5wqCF/xYOqEs754cNZz1pckznpFnPXFQ0KMFYKrqyvp6upSVlYWERHp6urSpUuXSFtbu4RLVrp4e3uTh4cH3blzh0JDQ2nfvn109uxZWrVqFW3fvp22bdtGRESCIIjvAUBERIGBgTRlyhTS19cvkbIXRCKRkFQqzff5nJwcsrS0JC8vL6pduzYFBATQ3bt3acyYMWRhYUGjR48mXV1d+ueff8TtFXV5iYhMTU1JR0eHTpw4UaS/ryQ8fvyYHj16REQkfjeCINDLly+pffv25OvrS926daOAgAA6d+4cEREFBARQVFQUJScnk6amJgGg9PR00tXVpQ0bNtDFixdLbH8KS37uyM8bufd/JiI6d+4cfffdd7Ro0SLas2cPLV26lBwcHOjw4cN08OBB6t27N4WEhNCRI0fy/X0aGhpftPyMFSXO+uLBWc9ZX1w46znrVQV3OrCPUrZsWUpOTqbz58/TwIEDqVq1aiVdpFJFT0+PunXrRrNnz6YffviBKlasSN9++y317NmTLCws6Pjx4wTZCCXxPfKKtajD+XNcv36djI2N6fz580qf19DQoJycHPrrr7+odevW5O/vT2vWrKEzZ87Q7t27KS0tjU6fPk2BgYFUu3Ztunz5crGUu1q1avT8+XOV/mw/RWxsLAUEBNDx48eJ6N2xs2vXLlq4cCHVqFGD4uLiaO3atZSYmEjz5s2jFy9eUIcOHUhbW5uGDRtGRLJj79atWxQQEEDa2tq0YsUKIqICG50l7cyZM/TLL7/Q69evxZ+JlDfuBUGg2NhYIiKqXr06NWnShCZOnEiNGjWiJk2aUHBwMF2/fp1MTEyKdycYK2Kc9UWLs56zvjhw1nPWq5SSGF7B1E9SUhJOnz6NzZs3w9raGu7u7rh27VpJF+urIL8vLjg4GLa2tiVcmg/Lb43rOnXqIDAwUOkQNPkQx6CgILi7uys8l5KSgrZt22Lnzp04e/Yszp07VzQFV+LVq1cwNDQUl4QrLaRSaZ4ZvA8fPozq1avDwMBAYdbplStXomHDhli0aBEAYMuWLdDS0kKDBg3QoUMHfPvtt9i4cSNWrVqFSpUqIT09vVj35UOSkpIwc+ZMcRm/jRs3QldXF7Nnz4aHhwcEQch3rfjjx49DR0cH8+fPR1JSEjIyMrB37164u7urzcRtjH0MzvqSw1nPWf+lcdZz1qsS7nRghRIXFwdvb2/Y2Nhg9uzZJV2cr879+/fh6uqKpUuXlnRRCkXesMjd6Dh69CgEQShwabBdu3bh22+/Rf/+/bF+/Xr8+eefaNCgAZo0aaJ0qaailpWVlWepJHWX+zu5du0apkyZIv48cuRI6OnpYcuWLeJjGRkZ6NKlC5o1ayZOIHXs2DFMmzYNXbp0waFDhwAAo0aNgqOjI54/f65Sy2Pt2LEDxsbGmDp1qviYubk5BEFAYGBggROHvXr1ClOmTEHZsmVhZmYGX19faGtri/e6yvG9nKy04KwvWZz1nPVfCmc9Z72q4U4HVmj//PMPr8VdjJKSkvDPP/9g8+bNqFmzJvz8/PLtpVUlq1evRt++fZU+16lTJzg4OOQb7tnZ2Vi3bh2srKxgZ2eHWrVqYciQIUVZ3EIpbctivXnzBgAwefJkWFtbY/PmzQCA+Ph4NGzYEP3790dKSor4+v3798PT0xPDhg1Tur1Tp07BwcEB48aNK/rCf4K+ffuiSZMmOHXqFOLi4tCkSRNUrVoVixYtEhsRBX2/Bw8exKZNmzB//nyFydtK0zHBmBxnffHirOesLyqc9Zz1qoQ7HRhTUbGxsXBycoKZmRl+/fXXki5OoWRnZ+OXX36Bubk5jh49Kj4md/v2bWhra2Pu3LkFbicjIwP3799XmEWdfRlHjx6Fq6srEhMTkZCQgICAAPj7++PZs2cAgClTpsDJyQkbNmxQeF9wcDDc3d1x48YNAEB6ejq2bduGoKAgaGlpITQ0VKWueuQ+7k6ePAkPDw+Eh4cjIyMDADBo0CA0aNAAJ06cyHcb+e2PsqXGGGPsU3DWc9YXBc56znpVw50OjKmwY8eOqeQVJ2UVtLxivnLlCvz9/dGyZcs8zwHA2LFjUaNGDTEQ8tsOV/SfL/f3JP88L126BEEQxPs8IyMj4ezsjFmzZgEAXr58iSZNmqBLly64ffu2+P579+7h8ePHCts/c+YMZs+e/cXXTP9SHj58iMDAQISHh8PGxgZ2dnbYvn07AODJkyewsLDA0KFDkZycXOht8nHJGPvSOOvZ5+Cs56xXB6VrmlbGShlPT0/S1NQs6WLkIZFI6MWLF/T3338TEVF2drY4I3CdOnWoTZs2dOfOHVq9ejURKc5w3LdvX3rz5g1t2LCBiPIuXyTfTu4ZhtnHycnJISLZ9/TgwQMikn2eAEhfX5+cnJxo7969RETUsWNHqlOnDsXExNCVK1eoQoUKFBwcTOfPnxe/IyIiY2NjqlatGkmlUvE7c3JyoqFDh5KlpWUx7+GHxcfHk7e3N+Xk5JCPjw8FBgZSQkICbd26le7fv0/6+vrUv39/+vPPP+nYsWNERPTy5Ut6/PgxESlfVouIj0vG2JfHWc8+BWc9Z7064U4HxthHe/v2LY0fP54aN25MAKhMmTIKy3v5+fmRq6srLVu2jF6/fi0uk0VEVL58eapZsyY9efKEiLhi/5LkDT4NDQ1KSUmhLVu2UPPmzWn48OGUkZFBgiCQhoYGZWVlid9VhQoV6Pvvv6esrCxat24dERF17tyZXF1dydHRMc/vkEgkavGdXb9+nVJTU2nOnDnUqlUrmjJlCk2bNo0uXrxIe/bsISKi4cOHk76+Pk2fPp369OlDenp69OuvvxIRH5eMMcZZr5o469/hrFcf3OnAGPto33zzDXXs2JEsLCxo1KhRRCQLQXnlbWhoSO3bt6fs7GyaOHEiEcnCkYgoMTGRUlNTyd3dvWQKX4rJ1+DeunUrmZiYUGxsLE2bNo3Wr19PYWFhdOvWLapcuTKZm5vTwYMHxfe1aNGCXF1dadeuXbRv3z4iIlq5ciW1atWqRPbjS7h8+TJVq1aN9PX1xUZXSEgI6erq0pYtW8T13+fOnUtt2rShJ0+e0J49e8SGCGOMfe0461UTZ/07nPXqo0xJF4AxppoAKO0Blj/u4OBAPXv2pKlTp9KAAQPI1NSUcnJyxN5xb29v6tixI0VERJCNjQ05ODjQ06dP6eeff6Z69epR3bp1S2CvSrcLFy7Qnj176O7du7Rq1Spq164dffPNN7Rs2TJaunQp+fv706ZNm8jd3Z0OHz5MycnJVLlyZSIiCgwMJA0NDapXrx4RvRuiqYpXAQoql/w5Hx8fGjNmDCUkJJCdnR1lZ2dTmTJlqH379jRhwgTatm0b2drakqOjIzk4OIjbk1/FkzfqGGOsNOOsVz+c9Zz16khAfjezMMa+SlKpVKyE5cMk5Vcu3hcfH0+9e/cmfX19io6OzhMQr169ov/973+0cuVK0tfXp1evXlGvXr1o9OjRRb8jpVzu70muf//+FB0dTdbW1nTkyBECIN4nnJaWRi1atKAqVapQQkICGRkZ0ZYtW+ibb75Rm9DNyclROBb//PNPcnFxoUqVKuV5bVZWFvn5+ZGmpibFxMSI+9ivXz/avXs31apVi5YsWUI2Njb5bp8xxkorznr1wFnPWV9acKcDY0ypmTNn0vHjx2nKlClij/j7cnJyaPPmzTRo0CCKjo4mX19fhcr84cOHVKNGDcrMzKRLly5RnTp16Ntvvy3O3Sh1cn++7zf8Lly4QD/99BOlpKTQlStXiIjEyaA0NDTo7t27tHfvXhowYAAREV27do1q1qypsB1lDRxVkLuM169fpxs3blCbNm3o999/pw4dOigtc1xcHDk5OVH79u2pRYsW9OrVK4qJiaFBgwaRo6MjGRoaFvduMMaYSuGsV02c9Zz1pY3qHW2MsRJ1+fJlcnBwoBUrVpCfnx9VqFBBvAryPg0NDWrcuLE4gZH8sZSUFJo2bRpZWlrSokWLSFNTk7777jtuhHwB8kbI4sWLqWvXrjRkyBA6c+YMERE5OjpS+/btSSKR0Pr164mIxAmliIhMTU2pX79+FBkZSebm5nT16lXxNXKq2AghkpXx3r175OnpSS1btqQdO3YQEdHy5cspMTExz+sBUL169Sg6OpoEQaCpU6fS3LlzqU+fPtS6dWsyNDRUmGmdMca+Jpz1qo2znrO+1PnSa3AyxtTbuHHj0LFjR3Hd5/fX6Va2dvH+/fthYGCABQsW4K+//oKlpSWqVKmCdevWFUuZS7P31zK/ePEinJ2dYWpqioiICDg7O6NJkybiZ3379m0EBASgTZs2SEpKAvDuO8z93RkZGSEqKirP46pswIABcHV1RUpKCm7duoXffvsNEokEc+fORWZmZoHvvXXrlsLP6rLPjDFWFDjrVQtn/Tuc9aUTdzowxkRJSUmoVq0ajh8/DgAYMWIEgoODMWzYMJw+fTrP6+WV+YsXLzB8+HAIggCJRIIRI0YUa7m/JsOGDUNwcLDYuDh79iwsLCzQsGFDseGxevVquLq64tdff813O+7u7pg/f36xlPljZGdn52kk5OTkICUlBZaWlvjf//6n8NyQIUNgZGSEy5cvK93e+9vKysr6sgVmjDE1w1mv+jjrOetLG9UcW8MYK3b4/5l8jY2N6caNG9SuXTs6deoUmZiY0M6dO6lfv37imsfyIZjyoXoVK1Ykf39/mjJlCj169IhmzJhRYvtRGs2ZM4dWrlxJRETdunWjsLAwkkgkNHr0aPL19SUjIyNKTU2lmTNnEpFsdmpbW1tau3atuFxUbp06daLz58+Tp6dnse7Hh+D/70cVBIHOnTtHhw8fpszMTJJIJKSnp0caGhr08uVLIiLKzMwkIqLx48fTy5cvaf369fTq1as823x/5usyZXjRJsbY14uzXnVx1nPWl2bc6cAYIyJZhZ2ZmUlSqZQOHDhAEomENm/eTBEREbR//35ycHCgsWPHEpHiDNf4/7loXVxcaNSoUaSvr18i5S8NlN13mJycTDExMRQfH09ERPb29mRnZ0ehoaF06NAhio6OpqNHj5KVlRVt27aNLl26RDo6OtS2bVsKDAwkU1NTcVvy78rX11e8n7ekPH78mB49ekRE7/ZbEAR6+fIltW/fnnx9falbt24UEBBA586dIyKigIAAioqKouTkZNLU1CQAlJ6eTrq6urRhwwa6ePFiie0PY4ypA876ksdZz1n/NeJOB8aYyNDQkNzd3em3334jTU1NqlGjBhERmZmZkb+/P0mlUjEU5OS9y6q4xrO6UTaxU+XKlUlbW5uSkpLEx+7cuUNbt26l4cOHU+PGjYlI1sh48OABhYeHExFRmzZtaNy4cVS+fPk82/zxxx/JwsKiiPbiw2JjYykgIICOHz9ORO/2e9euXbRw4UKqUaMGxcXF0dq1aykxMZHmzZtHL168oA4dOpC2tjYNGzaMiGTH3K1btyggIIC0tbVpxYoVRKS8QccYY0yGs75kcdZz1n+NeOwJY0zB5MmTae3atXT37l1KSEggS0tLIpIF3d27d5Wuk8y+DKlUSn379iV7e3vq0aMHVaxYkYiI/P39aezYsZSVlUVly5allJQUqlq1Kt2/f5+IiI4cOUJaWlo0a9YsMjc3F7clkUgUlsVSlcaivb09bdq0SeHKzF9//UX9+/cnALRw4UIyMTEhExMTun//Pi1btow2bdpEAwcOpFGjRlH37t3p8uXLZGJiQjExMbRixQqqV68eDRs2jDIyMkhLS6sE944xxlQfZ33J4aznrP8a8UgHxpiCChUq0PTp0ykxMZFmz55NSUlJ9OrVKzp+/Di1bNmSDAwMSrqIakk+3LEgEomETExMaPHixRQWFia+R1dXlwwMDMQhhebm5uTu7k4TJkwgd3d3atq0KTVs2JAGDx5Mbdq0EbeV+19VkZOTQ4IgkKmpKf333380depUIiLy8fGhHj160Nu3bxU+q65du5KVlRXt2rWLrly5QoGBgbR//34KCAggLS0t2r17N3Xp0oVu3LhBpqamlJGRwVc/GGPsAzjriwZnvQxnPcuj+OasZIypk7lz58LQ0BC2trYwMTGBjY0N/v3335IullrKvRTZvXv3kJ2dLf6sbDmnqKgo1K1bF61bt8bTp0+RmJiI8uXLIzY2VnzN06dPsX37dowbNw7x8fEFbk/VvHnzBgAwefJkWFtbY/PmzQCA+Ph4NGzYEP3790dKSor4+v3798PT0xPDhg1Tur1Tp07BwcEB48aNK/rCM8ZYKcJZ/+Vw1ivirGe5cacDYyxft2/fxsGDB7Ft27aSLorau3jxIry9vdGgQQN4eXlh1apVeV6Tu8ESHx+PunXrolmzZjh//jyaNm2KiIgIAMobG8qWn1JFR48ehaurKxITE5GQkICAgAD4+/vj2bNnAIApU6bAyckJGzZsUHhfcHAw3N3dcePGDQBAeno6tm3bhqCgIGhpaSE0NDTPOvOMMcY+jLP+y+Gsl+GsZ+9TrbE4jDGVYmZmRr6+vtShQ4eSLopawv8PHVy/fj01bdqUbG1tacaMGaSrq0tr1qzJMwOzfHikVColGxsbWrlyJRkYGJC3tzc9ePCAkpOTKTMzM8/9mlKpVFx+SpXkHvoo/ywqVapEp06doqysLLKwsKBmzZrRw4cPafXq1UREFBISQhUrVqQ///yT7ty5I75//PjxtG3bNrKysiIiIi0tLTI2NiZnZ2e6fPkyzZ07V+WGlzLGmDrgrP88nPWc9ezD+FtjjLEiIm8YREVFUZ8+fWjhwoXUuHFjGjVqFF2/fp309PSUvk8eqE5OTrRmzRpq27Yt/ffff2RhYUGampr5vl5VyNd2l0gk9ODBAyKSfRYASF9fn5ycnGjv3r1ERNSxY0eqU6cOxcTE0JUrV6hChQoUHBxM58+fpw0bNojbNDY2pmrVqpFUKhUbNU5OTjR06FBxAjTGGGOsuHHWc9azD1Oto5cxxkqZe/fuUVJSksJ655cvXyZ3d3e6d+8eJScnE5Hyyafkgb5w4UK6f/8+hYWFFU+hP5H8aoeGhgalpKTQli1bqHnz5jR8+HDKyMggQRBIQ0ODsrKyxP2tUKECff/995SVlUXr1q0jIqLOnTuTq6srOTo65vkdEolE5a7yMMYY+7px1nPWs4JxpwNjjBUhExMTsrOzo/Xr11N4eDi5ubnRoEGDKD09nTp06EAdOnSgK1eukCAIYsNDTt54qVixIhkaGpJUKlXp2ZrlV2G2bt1KJiYmFBsbS9OmTaP169dTWFgY3bp1iypXrkzm5uZ08OBB8X0tWrQgV1dX2rVrF+3bt4+IiFauXEmtWrUqkf1gjDHGPgZnPWc9Kxh3OjDGWBGbOHEiLVmyhDIyMkhDQ4MuX75Me/bsoZiYGNLR0aHJkycTESlcIVFGIpGo3PDK3C5cuECTJ0+mffv20apVq2jcuHHUpk0bWrZsGd25c4f8/f3pypUr5O7uThkZGeKVHyKiwMBA8vf3p3r16hHRuyGajDHGmDrgrOesZ/kTwN80Y4wVi9atW5OTkxNFRESIj/Xq1YseP35Mv//+O5UvX74ES/dxpFJpnkZR//79KTo6mqytrenIkSMEQLwvNS0tjVq0aEFVqlShhIQEMjIyoi1bttA333yj0o0rxhhj7GNw1nPWs7z422eMsWLw/PlzevDgAVlYWIiPZWZm0qNHj+i7775Tm0ZI7omj3u+z7tevH9WsWZNevHhBZcuWJU1NTZJKpZSTk0Pa2tq0bt068vPzo7i4OPrzzz/p3r17ebajykNKGWOMsYJw1nPWM+W404ExxoqBnp4e2dra0tSpU2nYsGG0c+dO8vb2psTERAoMDCzp4hWafFjo4sWLqWvXrjRkyBA6c+YMERE5OjpS+/btSSKR0Pr164mIxAmliIhMTU2pX79+FBkZSebm5nT16lXxNXJ8JYQxxpi64qznrGfK8TfOGGPFZNmyZdSoUSP6+++/afz48eTi4kKXLl0iBweHki5avuRXJuT/xsXFkYuLC82cOZNsbGzo1KlTNGbMGLHhERAQQDVr1qQtW7bQs2fPSBAE8YqGfBt9+vShzMxMysrKUnicMcYYU3ec9Zz1LK8yJV0Axhj7Wujo6NDSpUvp1atXBIAqVqxIRLJhjB+aWKqkyK9MyP9dv3492dra0t9//00SiYTatGlDQUFBtHDhQmrRogWZmZlR69atKTIyktauXUvh4eHiFY3cVzlMTU3pyZMneR5njDHG1BlnPWc9y4tHOjDGWDErX748VaxYkaRSKQFQ2UaI3Jw5c2jlypVERNStWzcKCwsjiURCo0ePJl9fXzIyMqLU1FSaOXMmEclmp7a1taW1a9fS5cuX82yvU6dOdP78efL09CzW/WCMMcaKC2c9Zz17hzsdGGOsmMl7+yUSiUr1/Cub2Ck5OZliYmIoPj6eiIjs7e3Jzs6OQkND6dChQxQdHU1Hjx4lKysr2rZtG126dIl0dHSobdu2FBgYSKampuK25EMrfX196fLlyyo91JQxxhj7HJz1nPXsHV4ykzHGWIHat29Penp6tHr1aiIiunPnDrm7u9O8efPEibHatWtHe/fupUaNGtH+/fuVbgeASjW8GGOMMSbDWc+KEo90YIwxRkSyqx99+vShBQsW0MuXL8XH/f396dChQ+JkUCkpKVS1alW6f/8+EREdOXKEtLS0aNasWTR48GBxW7n/JeL7ORljjLGSxlnPSgJ3OjDG2FegMIPaJBIJmZiY0OLFiyksLEx8j66uLhkYGNDFixeJiMjc3Jzc3d1pwoQJ5O7uTk2bNqWGDRvS4MGDqU2bNuK2cv/LGGOMsaLFWc9UFd9ewRhjpZxUKhUbBLlnz85vCOTvv/9OkydPJlNTU1q1ahVlZWVRrVq16Pjx42Rvb09ERElJSXTy5Em6cOECde3alWxsbArcJmOMMcaKDmc9U2Xc6cAYY1+Bixcv0ujRo6latWqkq6tL48aNI11dXYXX5G6wXL9+nQIDA6lGjRo0bdo0GjlyJLm5udH48eOVNjZycnJUbrIsxhhj7GvCWc9UFY+FYYyxUkrepxwZGUlNmzYlIyMjcnBwoB07dlCXLl3owIEDRCRrRBC9Gx4plUrJxsaGVq5cSQYGBuTt7U0PHjyg5ORkyszMzNPYkEqlpKGhwY0QxhhjrJhx1jN1wCMdGGOsFMvKyqIWLVpQy5YtaejQoUQku7LRvn17qly5Mu3bt4+0tbUVrny8r3v37rRx40aaPXs2hYWFFWfxGWOMMfYBnPVM1fFIB8YYK8UuXLhA165dE9fJzs7OJhsbG9LR0aGTJ0/StGnTiEj5bNPyqyILFy6k+/fvcyOEMcYYU0Gc9UzVcacDY4yVYg0aNKA3b97Q+fPnKTU1lcqUKUOnT58mbW1t6tWrF+3du5cSExOVNkTkk1BVrFiRDA0NSSqVKiyLxRhjjLGSx1nPVF2Zki4AY4yxoiORSGjatGk0fPhwOnz4MGlqatKuXbto1qxZZGJiQkePHqXExEQyNDQs1LYYY4wxplo465mq404Hxhgr5QYOHEiVKlWiM2fO0IsXLyguLo7s7OwoPT2dfvjhB/rmm29KuoiMMcYY+wyc9UyV8USSjDH2lXh/+avFixfT/Pnz6ciRI6Svr89XNxhjjDE1x1nPVBGPdGCMsa+EIAiUlZVFaWlpdPXqVVq9ejW1b9+eDAwMSrpojDHGGPsCOOuZKuKuLsYY+0oAoGPHjtHAgQPJx8eHPDw8aPr06SVdLMYYY4x9IZz1TBXx7RWMMfYVefDgAR07dow8PT3J2NiYiKjAdbsZY4wxpl4465mq4U4Hxhj7SuXk5JBEIlG6hBZjjDHG1B9nPVMF3OnAGGOMMcYYY4yxIsFjbBhjjDHGGGOMMVYkuNOBMcYYY4wxxhhjRYI7HRhjjDHGGGOMMVYkuNOBMcYYY4wxxhhjRYI7HRhjjDHGGGOMMVYkuNOBMcYYY4wxxhhjRYI7HRhjjDHGGGOMMVYkuNOBMcYYY4wxxhhjRYI7HRhjjDHGGGOMMVYkuNOBMcYYY4wxxhhjRYI7HRhjjDHGGGOMMVYk/g+IOj37qwMEewAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs = df_time_step_speedups[\n",
+    "    [\"time_step_solution_time\", \"linear_solver_time\", \"assembly_time\", \"dirichlet_time\"]\n",
+    "].plot(\n",
+    "    kind=\"bar\",\n",
+    "    rot=30,\n",
+    "    subplots=True,\n",
+    "    layout=(2, 2),\n",
+    "    sharex=False,\n",
+    "    sharey=False,\n",
+    "    legend=False,\n",
+    "    xlabel=\"\",\n",
+    "    ylabel=\"speedup\",\n",
+    "    ylim=0,\n",
+    "    title=\"Attention: linear_solver_time, assembly_time, dirichlet_time divided by iteration number!\",\n",
+    "    figsize=(12, 12),\n",
+    ")\n",
+    "for ax in axs.ravel():\n",
+    "    ax.grid(axis=\"y\", ls=\":\")\n",
+    "    ax.axhline(1, ls=\":\", color=\"k\")\n",
+    "    title = ax.get_title()\n",
+    "    if title != \"time_step_solution_time\":\n",
+    "        ax.set_title(title + \" (per non-linear iteration)\")\n",
+    "\n",
+    "axs.ravel()[0].get_figure().subplots_adjust(top=0.92, hspace=0.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "057dd79c-2c46-49c8-b41c-185f7bc72db7",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_time_step_speedups = df_median_time_step_timings.copy(deep=True)\n",
+    "# Attention: relies on right order of the dataframe index\n",
+    "assert np.all(df_time_step_speedups.index == [name for name, case in cases])\n",
+    "\n",
+    "for col in df_time_step_speedups.columns:\n",
+    "    c = df_time_step_speedups[col].values\n",
+    "    # c[:3] = np.max(c[:3]) / c[:3]\n",
+    "    # c[3:] = np.max(c[3:]) / c[3:]\n",
+    "    c[:3] = c[0] / c[:3]\n",
+    "    c[3:] = c[3] / c[3:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "32c2737a-7806-42a4-ba61-c54a5e3e1855",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "axs = df_time_step_speedups[\n",
+    "    [\"time_step_solution_time\", \"linear_solver_time\", \"assembly_time\", \"dirichlet_time\"]\n",
+    "].plot(\n",
+    "    kind=\"bar\",\n",
+    "    rot=30,\n",
+    "    subplots=True,\n",
+    "    layout=(2, 2),\n",
+    "    sharex=False,\n",
+    "    sharey=False,\n",
+    "    legend=False,\n",
+    "    xlabel=\"\",\n",
+    "    ylabel=\"speedup\",\n",
+    "    ylim=0,\n",
+    "    title=\"Attention: linear_solver_time, assembly_time, dirichlet_time **not** divided by iteration number!\",\n",
+    "    figsize=(12, 12),\n",
+    ")\n",
+    "for ax in axs.ravel():\n",
+    "    ax.grid(axis=\"y\", ls=\":\")\n",
+    "    ax.axhline(1, ls=\":\", color=\"k\")\n",
+    "    ax.set_title(ax.get_title() + \" (per timestep)\")\n",
+    "\n",
+    "axs.ravel()[0].get_figure().subplots_adjust(top=0.92, hspace=0.3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b25a5581-6c7a-4ef0-b908-f86405127f32",
+   "metadata": {},
+   "source": [
+    "# Number of linear solves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "13bf177b-1f11-45f7-bfa8-ca91beeee4b6",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "computes = []\n",
+    "solves = []\n",
+    "\n",
+    "for name, case in cases:\n",
+    "    outdir = get_out_dir(case)\n",
+    "    logfile = os.path.join(outdir, \"ogs-out.txt\")\n",
+    "\n",
+    "    res = subprocess.run(\n",
+    "        f'grep \"compute with Eigen .* linear solver\" {logfile} | wc -l',\n",
+    "        shell=True,\n",
+    "        check=True,\n",
+    "        capture_output=True,\n",
+    "    )\n",
+    "    comp = int(res.stdout)\n",
+    "\n",
+    "    res = subprocess.run(\n",
+    "        f'grep \"solve with Eigen .* linear solver\" {logfile} | wc -l',\n",
+    "        shell=True,\n",
+    "        check=True,\n",
+    "        capture_output=True,\n",
+    "    )\n",
+    "    solv = int(res.stdout)\n",
+    "\n",
+    "    computes.append(comp)\n",
+    "    solves.append(solv)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4f015316-b20d-4bae-b77d-76243c3b5776",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "df_solves = pd.DataFrame.from_dict(\n",
+    "    {\n",
+    "        \"computes\": computes,\n",
+    "        \"solves\": solves,\n",
+    "        \"case_name\": [name for name, case in cases],\n",
+    "    }\n",
+    ").set_index(\"case_name\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "03e8c460-18c8-4c47-ad2e-6a54ceca9c18",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f4b26979e50>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = df_solves.plot(kind=\"bar\", rot=30, logy=True, legend=False, xlabel=\"\")\n",
+    "ax.grid(axis=\"y\")\n",
+    "ax.set_ylim(0.5)\n",
+    "ax.legend(loc=\"upper left\", bbox_to_anchor=(1, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "c43b47dd-7912-46d0-bf08-033f22e43827",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "case_base = df_solves.loc[\"1 base\"]\n",
+    "assert case_base[\"computes\"] == case_base[\"solves\"]\n",
+    "\n",
+    "case_linear = df_solves.loc[\"2 linear\"]\n",
+    "assert case_linear[\"computes\"] == case_linear[\"solves\"]\n",
+    "assert 2 * case_linear[\"computes\"] == case_base[\"computes\"]\n",
+    "\n",
+    "case_linear_dt = df_solves.loc[\"3 linear & dt\"]\n",
+    "assert case_linear_dt[\"computes\"] == 1\n",
+    "assert case_linear_dt[\"solves\"] == case_linear[\"solves\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "75e2c4c4-845c-4431-9f42-b188d6752aa1",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "case_lu_base = df_solves.loc[\"4 LU\"]\n",
+    "assert case_lu_base[\"computes\"] == case_lu_base[\"solves\"]\n",
+    "\n",
+    "case_linear = df_solves.loc[\"5 LU & linear\"]\n",
+    "assert case_linear[\"computes\"] == case_linear[\"solves\"]\n",
+    "assert 2 * case_linear[\"computes\"] == case_lu_base[\"computes\"]\n",
+    "\n",
+    "case_linear_dt = df_solves.loc[\"6 LU & linear & dt\"]\n",
+    "assert case_linear_dt[\"computes\"] == 1\n",
+    "assert case_linear_dt[\"solves\"] == case_linear[\"solves\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c2dd7b34-0d3d-4baf-a86b-19e5387c8846",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": [
+    "case_dt_base = df_solves.loc[\"7 varying dt\"]\n",
+    "assert case_dt_base[\"computes\"] == case_dt_base[\"solves\"]\n",
+    "\n",
+    "case_linear = df_solves.loc[\"8 varying dt & linear\"]\n",
+    "assert case_linear[\"computes\"] == case_linear[\"solves\"]\n",
+    "assert 2 * case_linear[\"computes\"] == case_dt_base[\"computes\"]\n",
+    "\n",
+    "case_linear_dt = df_solves.loc[\"9 varying dt & linear & dt\"]\n",
+    "assert case_linear_dt[\"computes\"] == 2\n",
+    "assert case_linear_dt[\"solves\"] == case_linear[\"solves\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43168962-9627-4689-ab30-8a92d88b06f0",
+   "metadata": {
+    "jupyter": {
+     "source_hidden": true
+    }
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ogs-local-release-build",
+   "language": "python",
+   "name": "ogs-local-release-build"
+  },
+  "language_info": {
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+   "mimetype": "text/x-python",
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+}
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..692b44a36478dc3c1dcc9ae069a1f38de032117e
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA.xml b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA.xml
new file mode 100644
index 0000000000000000000000000000000000000000..e8605854f42be1326049e99d5997cf6d38098ee0
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA.xml
@@ -0,0 +1,122 @@
+<?xml version='1.0' encoding='ISO-8859-1'?>
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+                <repeat>10</repeat>
+                <delta_t>3.1536e8</delta_t>
+            </pair>
+            <pair>
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+                <delta_t>3.1536e9</delta_t>
+            </pair>
+        </timesteps>
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+
+    <remove sel="/*/time_loop/output/timesteps"/>
+    <add sel="/*/time_loop/output">
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+                <each_steps>10</each_steps>
+            </pair>
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+                <repeat>1</repeat>
+                <each_steps>9</each_steps>
+            </pair>
+            <pair>
+                <repeat>1</repeat>
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+                <repeat>1</repeat>
+                <each_steps>900</each_steps>
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+    <remove sel="/*/test_definition"/>
+    <add sel="/*">
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+            <vtkdiff>
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+                <field>[Cm-247]</field>
+                <absolute_tolerance>1.6e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Am-243]</field>
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+                <relative_tolerance>0</relative_tolerance>
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+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Pu-239]</field>
+                <absolute_tolerance>1.6e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
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+            <vtkdiff>
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+                <field>[U-235]</field>
+                <absolute_tolerance>1.6e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
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+            <vtkdiff>
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+                <field>[Pa-231]</field>
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+            <vtkdiff>
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+                <field>[Ac-227]</field>
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+                <relative_tolerance>0</relative_tolerance>
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+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>LiquidMassFlowRate</field>
+                <absolute_tolerance>8e-15</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
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+                <field>[Cm-247]FlowRate</field>
+                <absolute_tolerance>1e-15</absolute_tolerance>
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+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Cm-247]FlowRate</field>
+                <absolute_tolerance>1e-15</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Pu-239]FlowRate</field>
+                <absolute_tolerance>1e-15</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[U-235]FlowRate</field>
+                <absolute_tolerance>1e-15</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Pa-231]FlowRate</field>
+                <absolute_tolerance>1e-15</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Ac-227]FlowRate</field>
+                <absolute_tolerance>1e-15</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
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+</OpenGeoSysProjectDiff>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_0_t_0.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
new file mode 100644
index 0000000000000000000000000000000000000000..f6480176de3bd5a0b800027a083bd98ec2767293
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
@@ -0,0 +1,38 @@
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+  </AppendedData>
+</VTKFile>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_1009_t_3153600000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_1009_t_3153600000000.000000.vtu
new file mode 100644
index 0000000000000000000000000000000000000000..cc3a2977c457e9711a109eecea868db4e163f2f9
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_1009_t_3153600000000.000000.vtu
@@ -0,0 +1,38 @@
+<?xml version="1.0"?>
+<VTKFile type="UnstructuredGrid" version="1.0" byte_order="LittleEndian" header_type="UInt64" compressor="vtkZLibDataCompressor">
+  <UnstructuredGrid>
+    <FieldData>
+      <DataArray type="Int8" Name="OGS_VERSION" NumberOfTuples="26" format="appended" RangeMin="45"                   RangeMax="121"                  offset="0"                   />
+    </FieldData>
+    <Piece NumberOfPoints="601"                  NumberOfCells="600"                 >
+      <PointData>
+        <DataArray type="Float64" Name="LiquidMassFlowRate" format="appended" RangeMin="-7.1054273576e-15"    RangeMax="7.1054273576e-15"     offset="92"                  />
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+        <DataArray type="Float64" Name="[Ac-227]FlowRate" format="appended" RangeMin="-9.260082196e-23"     RangeMax="1.2050580963e-08"     offset="6912"                />
+        <DataArray type="Float64" Name="[Am-243]" format="appended" RangeMin="-1.2219107684e-50"    RangeMax="1"                    offset="10200"               />
+        <DataArray type="Float64" Name="[Cm-247]" format="appended" RangeMin="-2.4140561658e-47"    RangeMax="1"                    offset="16520"               />
+        <DataArray type="Float64" Name="[Cm-247]FlowRate" format="appended" RangeMin="-1.277333825e-22"     RangeMax="1.2114059092e-10"     offset="22800"               />
+        <DataArray type="Float64" Name="[Pa-231]" format="appended" RangeMin="-2.4465319572e-48"    RangeMax="1"                    offset="25660"               />
+        <DataArray type="Float64" Name="[Pa-231]FlowRate" format="appended" RangeMin="-9.8090153137e-23"    RangeMax="3.1284481874e-10"     offset="31960"               />
+        <DataArray type="Float64" Name="[Pu-239]" format="appended" RangeMin="-2.6292470643e-48"    RangeMax="1.0059797446"         offset="35016"               />
+        <DataArray type="Float64" Name="[Pu-239]FlowRate" format="appended" RangeMin="-5.772187954e-11"     RangeMax="1.2831001869e-22"     offset="41252"               />
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+        <DataArray type="Float64" Name="[U-235]FlowRate" format="appended" RangeMin="-2.3605501829e-10"    RangeMax="9.1052751822e-23"     offset="50604"               />
+        <DataArray type="Float64" Name="pressure" format="appended" RangeMin="100000"               RangeMax="100000"               offset="53452"               />
+      </PointData>
+      <CellData>
+      </CellData>
+      <Points>
+        <DataArray type="Float64" Name="Points" NumberOfComponents="3" format="appended" RangeMin="0"                    RangeMax="200"                  offset="53548"               />
+      </Points>
+      <Cells>
+        <DataArray type="Int64" Name="connectivity" format="appended" RangeMin=""                     RangeMax=""                     offset="59656"               />
+        <DataArray type="Int64" Name="offsets" format="appended" RangeMin=""                     RangeMax=""                     offset="61072"               />
+        <DataArray type="UInt8" Name="types" format="appended" RangeMin=""                     RangeMax=""                     offset="62328"               />
+      </Cells>
+    </Piece>
+  </UnstructuredGrid>
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diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_109_t_315360000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_109_t_315360000000.000000.vtu
new file mode 100644
index 0000000000000000000000000000000000000000..9b511565d8df76d4e0a88c5c48e8104c4360aebb
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_109_t_315360000000.000000.vtu
@@ -0,0 +1,38 @@
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xt4t89+AHfi7HE7M9mE6VfjV1bw9WzDzoOTj3wKgTeVnu6h6wc8mJ8GOwEGPGWL7dg4W9FPE3roVYCKBuzxwMwUKv4QzjbxssoIuc8Q5VsODZ5vWkgQcLETc7hB8RYsFGgzaxHrMLmeu3Mw66d4EvJdzgZ94utPZRL/UH7MJRloqii/kumG+MlGzL7YJtsT2/IMsu0OtP7ij+24HHRr/x+Bd2wEB6N2bj8w4oEaT12CfsgLHQtf4utx1o+5HdhdPcAWE83PquwA7wiYFBPckO2H0P03+8vQ0Pgmdpujq3oeqKTv/VrG0oS4qqoH+zDdQc9/Ev6W8Dp78R4RexbciyErxQpNyGDvR9wTwsBnpVHNenuzGgI3NktZSFAZt+Y6LP3hjAuJ3Q2+hhAFkreYgVxkAri9qmAikGNIq59Fy3tsDhkC3To3ULUL1FnFrJW0AReEvjitsWoI8yfMMebIE4ivsulmsLlG4FtLLhbYGJdEKZyOwmsO9MOzBUbYL8Qw6fxdBNKJVi1fMy34RX9rTeOMQm/LaqdJW9sQna5j1d9nsbQFn29dLLzg3gdJJt0E/dAGUz8dcMbhuQlCuM+XJ/A4hjryVIsW8A6zSWN+50HZ4T5CiNjq4Dplw9br9wHbi0/Np3367DxzFt7z7ddWjKUvIM5V+HVnVDJwH8dQiUPmOrm1uDMCSZNlvlGqTz8bS7Bq/B7zYiZMnTNVh+dze6T3QNMle9ooaJ1sDvvh1l8/IqpKzlHcTVrsJ7wffET8JWQadnSZnAdBWEKr5HpEusAt3J6wEO0lVgG9A6TlxdAZUJivOzuhUI+FC/qBG+At9LmhNiTFeALeqDYK/ECnAX7pcdkqyAEuG6PH37Mhhbswig7JdBwpztgTrNMrw8ZZxSbV0CtHTrNSm7JRDAqvHeurEE4SpWobivi2Attx/81XoRROYf+QdeXwSL57gt1cYFuG1hzkdmuQArl7UHR8kXwC33XDe5YR587N0NbM3mQXahyFmOdB5mLtalOevmYP5tvQmtyRzUeSSn0BPPwQlxarZwzSyQ1W6jnhnPQppIOTKfcBZerlO9JKqegXJ7xs6wpzNgoJBMJkQ4AwWI2Xt/qqZBUDTDcPfpNBQP1GhfJZqGTOSI2KOaKSiMjCbqfzYFsZ9ubHmRTMEJYTDmWf0kCPapCvmaT4IHTSlmkWISML5zst5NE7BAaeRqbDsBtg1O9cE0E0AcxPXorGMcdqlCg5ucx2EFHVUwxDwOMwX9V8SHxmC4yPP7d+8x8FXmHCC5OwYdITVH/vOj8EstocswbBRq01+XJSFGQcsXSy27NwIT+8MmWmkjYOwwQzChMQKP8UOcevFG4Nx6mNnKahjMBqiKBQaGIEy/2OxIZAgyuj7x1yYPwkQtcsf3yiDcOrzhYfhiAErbf1Xcn+mHh8bv7Z8o9EMx4a23IZXfoPeBVNUa8zfoU1DqeRHZB095YlyE8PrAz5XCVNilF/gRC1Iemz1wlOf3gdCgBxiz9daxw90Q8OvvJ27lbriuN6TR3tIFyjnK1O3SXdB2aijJX9sJLueWy0QineCF26U2rugAvdtCXYJCHfBUiq43oLIdigjSpszE2kHhS3bocEMbuGRRGI2g20AwGZFh190K5lPr/jkPW+GirIsXr7YFhF0m7YmHmmHT4a+s4+5X8Ijv65Ak/gqiu4Fl9vxN0C9lbUyj0wjZxyvDSJ8vcEKlkoAp+QwEJQ2zjIsNMF9j+HjregNsE1DoKqvVw8M0sRKloDpYss6r3umqBVTDTTVxoloIfZE/yP+oBvSP8G5PJ1eD8Ce7ALGdKvjLnjeqgqiC0kJdeZroSvjz/TAwfbcCaCYf1K0pV0BwZndlt2Y5EBbqtsgylMFpSjmI7JVCN3NrfmJnCey3WAk45RaDQ8vPmeqwInhNk2729FUh9FK4KVo6FcAlDc/29hf5MHix/e25Sx40559QPPbJhQyM+j/n6BwQpCRsrinJhsKWPRT5YBY8mw/jdviZCX5NCEw7RybgXfe0/lGbDgHOfBx/BdNgUnf8k2d9Cviqo/Sl1ZNBUREjQ3mQ+N/eRQUHUhOgMfHgUPpJPFichQhysMTBvJZyrPZxLMRTEU5GTMSAuk71VnJrNBy9Vf0j0xAFT4nH+U3dI4GfvfydR8wHaGXy4pLoDAOdC+d8WpJQWCDLZ/5oFQKGh4KxrFNB8ETLiE5MPxBA1YwSjXsHW/zGfhUJ/jA0/FfzTNMP1H4X+n9g9IXapsXD4h9vgIJgOsB/4DUEyGYPvOn0guK3d5Fecx6ARNALaJG5w/GPqrcYQ7f//kyCjr7LBRJQw2aDys6g+SA7eLjLEcxuiz/Jv/MCym8GU5w12oGdV4BZlYcN2GoHfP5iaAXyAvEEZFYW8Ob5dvinj2bQrl8dVbppAoV369k1yozB2zy7MmLIEOZPe85DefRB2jpsabhBFyiPUpqueeuAnqub0JmDFvji+eRHRzyCSB2X6JzyB6D9+0ZbR6QaSEHfDcoGZVAgKA7VYVeExhslc6kD8iBH58giVi4L1BWs7SofUBDPh7J7zigDCVeSiueuSoI/RhJHJy8GFB2XTxh7hYHGUSOtgEMQtkJwx1zifLCq6r8kX8cNI4bFIdppHNDvPh3gucIKAezWTSz7TEDu1EhuUkYPub09SLGfNBBg2UllX00NbCSVB2QLlJCtZlES1k0Ktf827xZ4E0J1CpHk9258MCKPFB6LvQRS3uk8M/l/0R66jANXcSfoa4slHTVbx2jK6VYtLo8j9NqXqieqdPvoj7aBTWHc22g9ilfqjufr6AaE6nP61yto+dNufFmaebTWGVu2hsAUmkqDul301hj6+TwRtfGLQbQm8zFp22QPOrhC/2kUogPt7zOxLIdtRiuZXg+PaGtAvxe3FuFvrkabnic6DhiVo4/D68RnhwrR6q+mpaNDc9DOP++fBBakowNnpuOjDhPQz27fPKdMi0H35rvmFo+Eo6OUdhGDokHoyED6U45rfmjXpoaV3X9e6Gj1QMrwFlf0ksLXkNL5F+h4TGlN80sr9Pkb/U61YRO0xz0JdQdCfbQ/6fWL8VAt9AN6g8hprfvoqICWRycECmhx7Oeb5YIoNGl9vPNNbnG0kexuR7m3AJqTgPXmlRlOdLpBDoMZEzP62cRaawUhDdqHpsox4QoF+s5KWI9+JQGaakWE9irPP1QSn1wmdvwY5W4zcRYUt4eKR976oum9jvq46vQs23wOxXldRD6gZRS1qLUZaVzbgxJsZUydcmlBoS6ry8TU1qD4T9Lx2qhLUBMWxuP3YrJQCUz/aSs7HpWf3WSbigtHKX2rlnhk4I8SaiKUornlgeKJLsUmTtmjbPKPUh7Mm6KKTeU/eh08RhWb65Wf1aqhVh3M7+SyyKJihxXv+WiIojb7r7wd3OZCEVfRcdMf0aMq36onBllSoCw9JSloWC6jyMUDp5Qbj5EEQr+80r0xSMQ5nly//hxSJ3B771hxENlL/ObxMV0rcrjeqVsmqBLJsMZb5bWajRRh5SD/w/IReXjRxUqACERa8TWvmnG5I2tzi5n65qyQa3RBjrqaesiSl45JZwHKSBldxohRF0mkXPFitzY9N3LnUtCI5xsaJF1BLU1+JgHSXNxd0yf0GOHUYavx4d46IuhIf3ZqchRxgXbbfKTeihjZoMjNs/iEUG8wdSHiSkbY6/wOrjoKQtg0Yf6Jz7kiXop1I3hxJgg1xYtLpl7qCAEzQ/60eEkEtvpM/r/qQ3xZchXFHZIjuGXnn6S9PpGJytVdfX59XYbqRk3vtvSgTNzSJOdzwjqZ7kL30NjHmTIE7M985YaDZaQGvRfHyZxlclO6hy3xnsikZMjpWcfLygiI3onQ8+eSIbQk+ERySibj793cQxX3Q3rNw+FsmWZWmvg0R7fWv0V6R/dwLsY8R1p0UE++2jhY+jBqqyQ99rn0DG/X/aLph9JxDYnbrGfC0hIe3zo55qilg5ROnW3sfkpxJXn1hUROSZFOhAnritRLEeU9Iqm/lyDFco3t2VCzu9SKOFnTpeLHUnlnnX8yaMSkVKzdbTiYqaS65dcjs3txkvbMbTc4qPslqRQdkPZm+ZK03MKXhL/7SVr9bqCsXjGU7JdCBG8di0kadmoWGLuTS/pPWX5NN92UyH8QIBP9o0lCrfEPf+9qjIRKvT2HRaW1xFrUqoxRBFKCOEc9IM6XUsLt69C/Jbk1ccmiF3a06tXiHIEh00nO78R5AT1Xm6UlzkXCWoePYxH34Wkd8mPYF3uNu0r5PrZerDvD+rfmvp+YEy9TY8IrVbE6zZhNmklKMWNSXYWzsAlR58vB/rh/8aIWlAmesiZ6otuBzsfXtW+KcuQwyAoIjIpMBQjcfbEdLKLa+0+1vxshEpguZWfvsCPMiKyauoqIEh56l+D0ypJN+Oezb3wvPdorvlynivGLfF6x7OHzkf4eldByhJKDmU+lID4HjxhHn1M53YLJSz2dq4L9vwh7nyx0CcyWyIyqmr0rK8x2zeqM5BEg0qVjalOf5P9y5elrk5bcT+kh2Pu0Is/4+eZOt+mGCD4pErLit2c0lt6jnL2vey36bpBs9GfxLq3SDbLKBj8tkrsqY4f6tvoVJUVNvEJdXm9KIhaZSHJ5tfgQAWP2LUO3Sr7wyH30mpi/U3OD71NmR1xxweWQ/Fcy4new7VXP7IRo7zgWjeBe9OCKDixjWQ2mGnnd30Uzm9C+K5rzKk86QN3j3TBdZIl1WS8kiVtgc347VdhPMzI/R1/Ho3JIRidGFFno/6JlPDjenIedvSJmekWyUGf411gpKRnPqnAuHuneagHPZutubmwdt1O4k0EP+YcCpu1KGmJKc278R2FrA7bSBb/mOjG2PNe5C424TVRsMPnpC5nmrPStXJcsZLLTH8XnH2ex3mhhceRKOhgNEJ1RzDfRoWCsmWbiQt51fHK49TPPmFM+TkttmLNF+YPKJe/8vKy1k/Cfsr6cXDakddq5enkEEl8LCtWFOV8OYh/yehHl4eUt2BAur3G8GDyUK2dryn167CpFzxDHUaNwkRa+6JhrYh8IYuIqHHwOTGm//dhyXe2w55dfn7NfuqnKvUw2lUPpTMJALlTJbrn8jeflr9AcqooUTayKJXuumdS46I5szsQnNI87CQN7h0iXdGPHz2wxXMBCWuYo20Ik6x+xptJsBg1Guo7oYLZ7+G7fb7mZZ1ffcS//yS3L9v7x1bGjC/rs6p6EU5+qX6yTHwTD84THs07/s36QdgXrgYj57aODsCyGtcnjzms2rGXMBkwyfQpZrrNr7x2p2VifPtgMYku+yHRLoAwf7Zhnoen8+jTgXUOmczsHPkdLHIs3Qf1FgqlrpqiR2lCzvibLtcfUPrKigpkVtL6zCXykLLl0O/I173cyLpN2fCRH9zArJJga9yLyMoj+nny+6/mOeZWctlBt1zRDOGDBLidUlvmJ6t+k0TymjPZNkuXI1+dMnN+2jL0/zKVH5Jvi7403ML2GX+91VBLS/Wf8DBk3XzLFkHtiPfN00kn9aloUAkWZEjt59g58qdKFU5d5TeKOGHW5mj12WkbSyAf5s23Pyxn7kbJVTqkRaZSvbKj0jF4wnh68RJ5LqafRK2E6MwX5GKNYcPdnUcRpy5sZVu9Md27vNq4KNCX2ptZr3+WaWS24nVZdqGKECE5tylJmPsmxup3Ymp3lS6+UWvyGqw+bxnl7uoi3TZ2fIFW3VC5ppGeDYak73qbRvjOl84JkyJIhl0E4hKlXZvxdyjn9xVBtrDlD1ZimoKnRvZQNk+uJE3fZGcgbiyeq8a+kjLk4ff20vkY/ZoBQft3Xnlz2Z+3Dcm02/aS0pUptoX/yJW4OkeFsM/rFvfGfW9XyyRKj//olp9np/wcsnw12AQAAAAAAAAAAgAAAAAAAAMgSAAAAAAAAiBIAAAAAAAA=eF4Vlnc81I8fx2UTlVX2XpkXZxx3nzeJshWV7LJKMkJWA6HskSSbsxNCWdkjZFOIZHOcu0KFjN/39/rz9dfz8frn+aKg+H9+QnNs82y6gmsqw8vIU5mbZOCUrFriWiIDS6mblNc4GbZ1WiKLe8iQw8z/7mMDGe6NUemWviUDB8lh3zeLDNQnirwEEshQjcJfzQ0hg2CwkB6FNxm0RYi7qo5kOFTGMhlcI4MW+YM45iIZ2IK2yEcqZDilt8eULUkGZ/8Sfi4uMth5m/a4MJAhYUejMmmXBCGmrwKSCSTQPGnf4T5Jgu9tnCjBHhI8Zz1hW1hLgp/VtL+oi0jg484RqvqKBPjRgTrNcBLoJQooCPqQwHp47PmoPQlSzZIsLK+QgInn94UaDRL0ri8vLMuRYItbe4zASwJGH/13zYwkiGdPYr+9swESyhkp80sboBcyOiE3ugHidPTuBi0bEJrvtqX2dgM+fRE52n29AYLuKjIR4RtgsCDCtnp/A45b9+udtt2AJtbg22z6G6AscIzmh/IGxC5x1PsJb0BItjffLPMGCIvqtLPvEsG15JzW6UUiOAy7KC4OEOFGX4rk4zoiyG3v1i/gidAJFsHssUSoWWdnOeVHBIPl1xRfbxKhVHln0VGfCLqto5eb0UTwWfafmeMjQgO1q0g/LRHqO63fB5HXIb8WJbU/vg676AQeaFkHei8fAd2ideik4Oznil+HgdfsjRW+61A59iOc2XYdeqbN2+V11uHYWD/TGdl14PCO229j+69H3ZFV3FuDI+EWdefZNcAcnGy36VqDYceHdlyla8CcZlWRnLAGoUwtJl8frEHpm/2Fccs1YP/XfzxNYw2IBlNOAmJrMOhRmOrAsAZWj00NXDYIUOseLogaJsDiWnTL+2oCKA7d+bL3igCEE5t0RwEEKHlfQ9NmTYCLGZ2PtTQIcNujXPCpMAEuX6HPDKImgHowQ6n68iosHpyjqP60Cr8uq14lFK1CyKqp01TEKnAxqpEjXFahiTehaltvFd6YWPjwSK9C0kLP7gHjKvyWJBxPX1uBdFSv7++eFWCO4mBmLF6BPzv16ZPPVuAc8/iBvdMKZD4OOF58YQXYPT/kFQmvwCbjQOktihXgPR1GO/59GRxeVGfQNCzD0NNj9qRXy5BZW6aT4L0Mt9KtNUgmyyAms21EI7sMI0r83l/plwEr0/Th5uISiOWLiRQ2L8H1SanPBalLELVZ1W3nswShLxUwX4yXwDefT4NaaglaSoXoSFRL0ECMb0n8vgi9hsb5mx8WgVqsYJM5fhHo8J3ExduL0PwONeeruQiPtSpFP3EtAko7kG301wK8GLFnSe9ZAOtkxkjhnAX4nGiy5eS3AFf2j/fcMV4AheTiBGnxBXCPUpgu3Z8HaTc3DdLIPPzpv3rzZ9E8IPH061WP5+HiGboXGLN5aDTDnA45Ow/yQzEQezgHcySBRZvROQjj75ndLpyDEMlzQpcfzoF0dmiat8kcSHK9NbETnQN4EWHAsTMLdgSegqTPs2DL+uPpj8xZKEzt4vjrOQtUqg9eT1+YhZvWzywSzszC3YmT1rJjP+Dv35jNKb8fcJpr8sY5vh/A+dE0HtU6AxOJBaXTDjPg1bW3rEo/A2ZfDRIuvfkOgruDbCeNvoNoS9NO3M9p2CiuWx1InAY2VhX8IHoaWOU5GZO/TkGTtAa3uN8UdMdnrD/lmoLjK+9qq+q/wZWr0uN1lt/gZVxwQ+r+JMy+cfhtkTEJqq0Sike4SRCpkNCOnpkAY24bNronE5DzzAHvLTAByHoA17fmcaCuHPyAtRsHtTSVbzkU4zCouPHvRM5XCL4wWxWh+RVs6PCVZ+a/QHgnS0dTyBcwKNsZDhX5AgdGa9PuHWOgrJN+GOY4BgVPu536acfg2tU9G4OiUThooNJn0BsFnItEDiNxBE5LaBCvxY7A9peM3m3UCNwaHSKujQ5DjbM9u4bvMEgYYjn+cQ9DWgtqWLh5CAaiKtBN9kPwU3NBdYx+CMxPnmkbtxqEe47j3pzFAzDpLlrh96cffF625dBq94OHWkzgUFIf/PNhc19c+gxU44JVJqqfoW9+wkMkuhdkx153u8z1APMTpX60ag+MVos/iIvvhu5nhfjna5/gxTsuLjmdTyCIOecTg+8CLWFuqWrKrv984hdcbt8JMuNCCdFdHUCX6I4yl+4AB+mfh/yJ7TDhyHZlY68NWu2PYwYd26D+m6TP0EgrGLHpu+5rtsJPl3eyNlUtUOyUc5tCogXORon2f8M0g3QaNY2NVBNcYKMyzBBohGhGpsQSro+gmazUnszZAAqnpPs9eevhBzs5Tk+sDkqrxyblFGuhM73aVFqnBrJiqB7p23yANwW+pa8D34O/wuY2f2Y1BLuuSy90VsH5DoMZ4mYlPF/X1dUUqwS9GD3rRat3YFhnXTrxugImbL+PIbXlQHy7/M3jYhlURvebXZgrBWNhTruqsDdQqJ1l8B5dAirnGPAIqQgSbrwX1KwsBCadR61vggpATmKlx+VGPgREfGlxxuZBTqXsw4izeOD2QpBqwVywkeas6hHKgQ6RkGPFMtmQWyfebh2SCX4Cg853m9IhkhYRl2ZOAwr8cM8lt9fw/r4/pevcKzhIC4tRdUoGo/rrt4z2kyCSgUSnh38BWeeF+jssEoG7YuTITDgBns1h3Xm34mBl1AtD1RIDIrmF7g9LogD9e+Z27rsIcKPUMIgeewahA2aNRqzhkHrK4sH+nVDQHg4aL/gWAoHEIS8b+2DIK3duUaQKgilyD7jffwQThwWEv/SBsJ183Eqy2w8+76UUSZc9gPPyU1IKdd6Auevid3/lPuBXTH2kVDyh63qTqYahO0zxnlTRvu0K9yrTL3B8vAOjEuoWvtrOsHVNou/SMUcoyjIqeEy8BbwOm9Xi1DfhpZYs/XCkDdypenidydISchtll+u8zKFIiO3958GrkO4/uHXXxRT2o5riDpDLkFfCUqKaaAQtRjUDGAZ9SPkXs8s5dRFmXYzf2VFowxy/Ez3j/fPQUkZXFnBWA0bO8CjRaGKhPdS3tzJXFX746xUq31OC5zWZ9NHJCvBEpHXkDgcKTLLC9R+0SQOpFS0hvygBp18HMj4IFoVqWt2/e9FCsGvpnC/1mw/+XBcmOD7mAuyTrsTYHA445mH3z9uAFdL0VRrsv58AyxT/jeNbDGAkcy3Lu5kGSspkFKikKQGdkvfGRfMQMbwhf2qYZxfJvTx441LHNnJG8+V6H98vRDFOenWIj4jYhj2SL6dcQSref5e72z6P3GHzGqx5OIOcVXirOVQ6gcja3AygXBpFZGYWI/K3B5H397qTWON7EJ/CA8UR6w7ER3Fy57dkC1Jjse1WGlGHXOMKpJo9U42wvDP5HRtYjiz078pdjC1CRDjrG/RY8IjCjyTJ8wEZSIFeRJhcWTJC4h1PsbdJQKIKar5n+UciDNFUEUHsoQhvlg6nnPFjRF6J/19s2wOEOTdM8oDCE0EFjNz01rmDyNq7+6aX30IspzVWhAItkUXlMvwhpxli2sR7t1jdENEOraSjFdVGWK83PZt3QhC+hxZnUxqUkCtxO9dRC3JIUliKHGpYDJnY8AmbzuFHes/mo3/ZcCAHngf/fHKYER5bEeqJ29TIOG1xTB3pH06HthO69jdxNdXTW814As6SiwZjdG4Ox5qay+S19BX31EZnvkFnAKf5UEQm80c7boHF3EV9uQ7X0FTwSL6lAodRwcjuB+Xjgpj9LEcc0nB+XU/5D40TcENfDriC3ofhyqid1KopAnHEz6+xFEUeOBZcSEiWnQPuoHc5UajWHIfVZqGLrDfE0QV02/vXauJIXG6g+hONW/2YEvEgVBKH6FQJUd/nxYW5zEvR3DiFI+jHY0bSKXF3R855De5vYx0WV5iU3FewFjq7t8VWJ7GPfKlOrN7ow870Dt/Wfd+EtbV7hT7pU4H1oPSv2fLKwf7HqKBjnIj9M7Qf9+IoBLuOak91fuyFjd3KnC7tssfCyoXWwzFTbFgxza2VQi1sn2woiVJDEWv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+  </AppendedData>
+</VTKFile>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_10_t_3153600000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_10_t_3153600000.000000.vtu
new file mode 100644
index 0000000000000000000000000000000000000000..9e82a52d609be85cc6c69585eb2d6c4f1bde4117
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_10_t_3153600000.000000.vtu
@@ -0,0 +1,38 @@
+<?xml version="1.0"?>
+<VTKFile type="UnstructuredGrid" version="1.0" byte_order="LittleEndian" header_type="UInt64" compressor="vtkZLibDataCompressor">
+  <UnstructuredGrid>
+    <FieldData>
+      <DataArray type="Int8" Name="OGS_VERSION" NumberOfTuples="26" format="appended" RangeMin="45"                   RangeMax="121"                  offset="0"                   />
+    </FieldData>
+    <Piece NumberOfPoints="601"                  NumberOfCells="600"                 >
+      <PointData>
+        <DataArray type="Float64" Name="LiquidMassFlowRate" format="appended" RangeMin="-7.1054273576e-15"    RangeMax="7.1054273576e-15"     offset="92"                  />
+        <DataArray type="Float64" Name="[Ac-227]" format="appended" RangeMin="-9.6616858686e-117"   RangeMax="1"                    offset="596"                 />
+        <DataArray type="Float64" Name="[Ac-227]FlowRate" format="appended" RangeMin="-1.2570843099e-22"    RangeMax="1.2096330906e-08"     offset="7068"                />
+        <DataArray type="Float64" Name="[Am-243]" format="appended" RangeMin="-1.0859646742e-113"   RangeMax="1"                    offset="8920"                />
+        <DataArray type="Float64" Name="[Cm-247]" format="appended" RangeMin="-1.5775978113e-113"   RangeMax="1"                    offset="15392"               />
+        <DataArray type="Float64" Name="[Cm-247]FlowRate" format="appended" RangeMin="-1.0706819054e-22"    RangeMax="3.9632086893e-09"     offset="21856"               />
+        <DataArray type="Float64" Name="[Pa-231]" format="appended" RangeMin="-1.4545214007e-113"   RangeMax="1"                    offset="23740"               />
+        <DataArray type="Float64" Name="[Pa-231]FlowRate" format="appended" RangeMin="-9.028547139e-23"     RangeMax="3.9711458768e-09"     offset="30212"               />
+        <DataArray type="Float64" Name="[Pu-239]" format="appended" RangeMin="-1.8817994042e-113"   RangeMax="1"                    offset="32124"               />
+        <DataArray type="Float64" Name="[Pu-239]FlowRate" format="appended" RangeMin="-1.4206827021e-22"    RangeMax="3.9386787088e-09"     offset="38596"               />
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+        <DataArray type="Float64" Name="[U-235]FlowRate" format="appended" RangeMin="-1.1853562801e-22"    RangeMax="3.9523502491e-09"     offset="46944"               />
+        <DataArray type="Float64" Name="pressure" format="appended" RangeMin="100000"               RangeMax="100000"               offset="48848"               />
+      </PointData>
+      <CellData>
+      </CellData>
+      <Points>
+        <DataArray type="Float64" Name="Points" NumberOfComponents="3" format="appended" RangeMin="0"                    RangeMax="200"                  offset="48944"               />
+      </Points>
+      <Cells>
+        <DataArray type="Int64" Name="connectivity" format="appended" RangeMin=""                     RangeMax=""                     offset="55052"               />
+        <DataArray type="Int64" Name="offsets" format="appended" RangeMin=""                     RangeMax=""                     offset="56468"               />
+        <DataArray type="UInt8" Name="types" format="appended" RangeMin=""                     RangeMax=""                     offset="57724"               />
+      </Cells>
+    </Piece>
+  </UnstructuredGrid>
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AQAAAAAAAAAAgAAAAAAAAMgSAAAAAAAAzxIAAAAAAAA=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diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_19_t_31536000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_19_t_31536000000.000000.vtu
new file mode 100644
index 0000000000000000000000000000000000000000..5a3f6808efc11e55169604a8155e9c698beb753d
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_GIA_ts_19_t_31536000000.000000.vtu
@@ -0,0 +1,38 @@
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+  </AppendedData>
+</VTKFile>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_ReactiveDomain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_ReactiveDomain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..13f06137f4412db5e3ea84848af7f3ae8e27fc15
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_ReactiveDomain.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_ReactiveDomain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_upstream.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_upstream.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..436686ec91dbcfd3cf92c096e3ea2dedf07cab6a
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt/1d_decay_chain_upstream.vtu
@@ -0,0 +1 @@
+../1d_decay_chain_upstream.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..6dfc0ea7268f622f25268e90a8b117db2af8e1f0
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA.xml b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA.xml
new file mode 100644
index 0000000000000000000000000000000000000000..c68801d2a628cc8769bde37721260f18fceace83
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA.xml
@@ -0,0 +1,126 @@
+<?xml version='1.0' encoding='ISO-8859-1'?>
+<OpenGeoSysProjectDiff base_file="../1d_decay_chain_GIA.prj">
+    <add sel="/*/processes/process">
+        <is_linear>true</is_linear>
+    </add>
+
+    <remove sel="/*/time_loop/processes/process/time_stepping/timesteps"/>
+    <add sel="/*/time_loop/processes/process/time_stepping">
+        <timesteps>
+            <pair>
+                <repeat>10</repeat>
+                <delta_t>3.1536e8</delta_t>
+            </pair>
+            <pair>
+                <repeat>999</repeat>
+                <delta_t>3.1536e9</delta_t>
+            </pair>
+        </timesteps>
+    </add>
+
+    <remove sel="/*/time_loop/output/timesteps"/>
+    <add sel="/*/time_loop/output">
+        <timesteps>
+            <pair>
+                <repeat>1</repeat>
+                <each_steps>10</each_steps>
+            </pair>
+            <pair>
+                <repeat>1</repeat>
+                <each_steps>9</each_steps>
+            </pair>
+            <pair>
+                <repeat>1</repeat>
+                <each_steps>90</each_steps>
+            </pair>
+            <pair>
+                <repeat>1</repeat>
+                <each_steps>900</each_steps>
+            </pair>
+        </timesteps>
+    </add>
+
+    <remove sel="/*/test_definition"/>
+    <add sel="/*">
+        <test_definition>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Cm-247]</field>
+                <absolute_tolerance>1.6e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Am-243]</field>
+                <absolute_tolerance>1.6e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Pu-239]</field>
+                <absolute_tolerance>1.6e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[U-235]</field>
+                <absolute_tolerance>1.6e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Pa-231]</field>
+                <absolute_tolerance>1.5e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Ac-227]</field>
+                <absolute_tolerance>2.6e-9</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>LiquidMassFlowRate</field>
+                <absolute_tolerance>8e-15</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Cm-247]FlowRate</field>
+                <absolute_tolerance>3e-10</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Cm-247]FlowRate</field>
+                <absolute_tolerance>3e-10</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Pu-239]FlowRate</field>
+                <absolute_tolerance>3e-10</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[U-235]FlowRate</field>
+                <absolute_tolerance>3e-10</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Pa-231]FlowRate</field>
+                <absolute_tolerance>3e-10</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Ac-227]FlowRate</field>
+                <absolute_tolerance>3e-11</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+        </test_definition>
+    </add>
+</OpenGeoSysProjectDiff>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_0_t_0.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..65369967d342e460e83ce2c6e0241aadafae97f9
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_1009_t_3153600000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_1009_t_3153600000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b94fdb1ff141ae622a36a5a314104ed846819652
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_1009_t_3153600000000.000000.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_GIA_ts_1009_t_3153600000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_109_t_315360000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_109_t_315360000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..91c9716f9b1c6ac61e49cef5ba65ba4dd40da2c6
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_109_t_315360000000.000000.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_GIA_ts_109_t_315360000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_10_t_3153600000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_10_t_3153600000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..6a0f0027d52390a52e5cd846be3f60de1a8d577f
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_10_t_3153600000.000000.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_GIA_ts_10_t_3153600000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_19_t_31536000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_19_t_31536000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..0dc79c33fc3a01cc2df7ddf365936e408e235728
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_GIA_ts_19_t_31536000000.000000.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_GIA_ts_19_t_31536000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_ReactiveDomain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_ReactiveDomain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..be15462f2a49cd0d5502ec7d6dfc3238df397526
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_ReactiveDomain.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_ReactiveDomain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_upstream.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_upstream.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..1330f8997037ea712f875e0bf60e286e52843763
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear/1d_decay_chain_upstream.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_upstream.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..6dfc0ea7268f622f25268e90a8b117db2af8e1f0
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml
new file mode 100644
index 0000000000000000000000000000000000000000..4ec9cc4fc40a7a636781720d822abf40c35490f1
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA.xml
@@ -0,0 +1,127 @@
+<?xml version='1.0' encoding='ISO-8859-1'?>
+<OpenGeoSysProjectDiff base_file="../1d_decay_chain_GIA.prj">
+    <add sel="/*/processes/process">
+        <is_linear>true</is_linear>
+        <linear_solver_compute_only_upon_timestep_change>true</linear_solver_compute_only_upon_timestep_change>
+    </add>
+
+    <remove sel="/*/time_loop/processes/process/time_stepping/timesteps"/>
+    <add sel="/*/time_loop/processes/process/time_stepping">
+        <timesteps>
+            <pair>
+                <repeat>10</repeat>
+                <delta_t>3.1536e8</delta_t>
+            </pair>
+            <pair>
+                <repeat>999</repeat>
+                <delta_t>3.1536e9</delta_t>
+            </pair>
+        </timesteps>
+    </add>
+
+    <remove sel="/*/time_loop/output/timesteps"/>
+    <add sel="/*/time_loop/output">
+        <timesteps>
+            <pair>
+                <repeat>1</repeat>
+                <each_steps>10</each_steps>
+            </pair>
+            <pair>
+                <repeat>1</repeat>
+                <each_steps>9</each_steps>
+            </pair>
+            <pair>
+                <repeat>1</repeat>
+                <each_steps>90</each_steps>
+            </pair>
+            <pair>
+                <repeat>1</repeat>
+                <each_steps>900</each_steps>
+            </pair>
+        </timesteps>
+    </add>
+
+    <remove sel="/*/test_definition"/>
+    <add sel="/*">
+        <test_definition>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Cm-247]</field>
+                <absolute_tolerance>1.6e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Am-243]</field>
+                <absolute_tolerance>1.6e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Pu-239]</field>
+                <absolute_tolerance>1.6e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[U-235]</field>
+                <absolute_tolerance>1.6e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Pa-231]</field>
+                <absolute_tolerance>1.5e-8</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Ac-227]</field>
+                <absolute_tolerance>2.6e-9</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>LiquidMassFlowRate</field>
+                <absolute_tolerance>8e-15</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Cm-247]FlowRate</field>
+                <absolute_tolerance>3e-10</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Cm-247]FlowRate</field>
+                <absolute_tolerance>3e-10</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Pu-239]FlowRate</field>
+                <absolute_tolerance>3e-10</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[U-235]FlowRate</field>
+                <absolute_tolerance>3e-10</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Pa-231]FlowRate</field>
+                <absolute_tolerance>3e-10</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+            <vtkdiff>
+                <regex>1d_decay_chain_GIA_ts_[0-9]*_t_[0-9]*.000000.vtu</regex>
+                <field>[Ac-227]FlowRate</field>
+                <absolute_tolerance>3e-11</absolute_tolerance>
+                <relative_tolerance>0</relative_tolerance>
+            </vtkdiff>
+        </test_definition>
+    </add>
+</OpenGeoSysProjectDiff>
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_0_t_0.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..65369967d342e460e83ce2c6e0241aadafae97f9
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_GIA_ts_0_t_0.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_1009_t_3153600000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_1009_t_3153600000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..b94fdb1ff141ae622a36a5a314104ed846819652
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_1009_t_3153600000000.000000.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_GIA_ts_1009_t_3153600000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_109_t_315360000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_109_t_315360000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..91c9716f9b1c6ac61e49cef5ba65ba4dd40da2c6
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_109_t_315360000000.000000.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_GIA_ts_109_t_315360000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_10_t_3153600000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_10_t_3153600000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..6a0f0027d52390a52e5cd846be3f60de1a8d577f
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_10_t_3153600000.000000.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_GIA_ts_10_t_3153600000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_19_t_31536000000.000000.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_19_t_31536000000.000000.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..0dc79c33fc3a01cc2df7ddf365936e408e235728
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_GIA_ts_19_t_31536000000.000000.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_GIA_ts_19_t_31536000000.000000.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_ReactiveDomain.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_ReactiveDomain.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..be15462f2a49cd0d5502ec7d6dfc3238df397526
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_ReactiveDomain.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_ReactiveDomain.vtu
\ No newline at end of file
diff --git a/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_upstream.vtu b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_upstream.vtu
new file mode 120000
index 0000000000000000000000000000000000000000..1330f8997037ea712f875e0bf60e286e52843763
--- /dev/null
+++ b/Tests/Data/Parabolic/ComponentTransport/ReactiveTransport/DecayChain/GlobalImplicitApproach/varying_dt_is_linear_compute_only_on_dt_change/1d_decay_chain_upstream.vtu
@@ -0,0 +1 @@
+../varying_dt/1d_decay_chain_upstream.vtu
\ No newline at end of file
diff --git a/scripts/cmake/test/NotebookTest.cmake b/scripts/cmake/test/NotebookTest.cmake
index b3bf0d4917d2171f3b848e21f68aa4e09a578a84..d42c2c58bf90b3cb940c2722fa488e2428bd1d3f 100644
--- a/scripts/cmake/test/NotebookTest.cmake
+++ b/scripts/cmake/test/NotebookTest.cmake
@@ -5,7 +5,7 @@ function(NotebookTest)
         return()
     endif()
 
-    set(options DISABLED)
+    set(options DISABLED SKIP_WEB)
     set(oneValueArgs NOTEBOOKFILE RUNTIME)
     set(multiValueArgs WRAPPER RESOURCE_LOCK PROPERTIES LABELS)
     cmake_parse_arguments(
@@ -54,9 +54,12 @@ function(NotebookTest)
 
     set(TEST_NAME "nb-${NotebookTest_DIR}/${NotebookTest_NAME_WE}")
 
-    set(_exe_args Notebooks/testrunner.py --hugo --out ${Data_BINARY_DIR})
-    if(DEFINED ENV{CI})
-        list(APPEND _exe_args --hugo-out ${PROJECT_BINARY_DIR}/web)
+    set(_exe_args Notebooks/testrunner.py --out ${Data_BINARY_DIR})
+    if(NOT NotebookTest_SKIP_WEB)
+        list(APPEND _exe_args --hugo)
+        if(DEFINED ENV{CI})
+            list(APPEND _exe_args --hugo-out ${PROJECT_BINARY_DIR}/web)
+        endif()
     endif()
     list(APPEND _exe_args ${NotebookTest_SOURCE_DIR}/${NotebookTest_NAME})
 
diff --git a/web/content/docs/devguide/documentation/jupyter-docs/index.md b/web/content/docs/devguide/documentation/jupyter-docs/index.md
index d52a31b31c637e04b4c2a59e241a61419416e2e1..bd94e384ed5ae1089686bb44a11ba07b8b90fb86 100644
--- a/web/content/docs/devguide/documentation/jupyter-docs/index.md
+++ b/web/content/docs/devguide/documentation/jupyter-docs/index.md
@@ -181,6 +181,12 @@ if(NOT OGS_USE_PETSC)
 endif()
 ```
 
+<div class='note'>
+
+If your notebook should **not** appear on the website add the `SKIP_WEB`-option to `NotebookTest()`. This may be useful if the notebook serves as CI test only, e.g. comparing multiple simulation runs or doing performance measurements. But please also note that there will be no artifact produced (except for notebook errors which get reported as usual).
+
+</div>
+
 Then e.g. run all notebook test (`-R nb`) in parallel (`-j 4`) with:
 
 ```bash