fixes for TOMS JURECA example

Author: pancetta

pySDC/implementations/problem_classes/HeatEquation_2D_PETSc_forced.py

@@ -68,13 +68,16 @@ def __init__(self, problem_params, dtype_u=petsc_data, dtype_f=rhs_imex_petsc_da
         # setup solver
         self.ksp = PETSc.KSP()
         self.ksp.create(comm=self.params.comm)
-        self.ksp.setType('cg')
+        self.ksp.setType('gmres')
         pc = self.ksp.getPC()
         pc.setType('none')
-        self.ksp.setInitialGuessNonzero(True)
+        # self.ksp.setInitialGuessNonzero(True)
         self.ksp.setFromOptions()
         self.ksp.setTolerances(rtol=self.params.sol_tol, atol=self.params.sol_tol, max_it=self.params.sol_maxiter)
 
+        self.ksp_ncalls = 0
+        self.ksp_itercount = 0
+
     def __get_A(self):
         """
         Helper function to assemble PETSc matrix A
@@ -188,6 +191,8 @@ def solve_system(self, rhs, factor, u0, t):
         me = self.dtype_u(u0)
         self.ksp.setOperators(self.Id - factor * self.A)
         self.ksp.solve(rhs.values, me.values)
+        self.ksp_ncalls += 1
+        self.ksp_itercount += int(self.ksp.getIterationNumber())
 
         return me
 

pySDC/projects/TOMS/pySDC_with_PETSc.py

@@ -47,7 +47,7 @@ def main():
     level_params = dict()
     level_params['restol'] = 1E-08
     level_params['dt'] = 0.125
-    level_params['nsweeps'] = [1]
+    level_params['nsweeps'] = [3, 1]
 
     # initialize sweeper parameters
     sweeper_params = dict()
@@ -62,7 +62,7 @@ def main():
     problem_params['freq'] = 2  # frequency for the test value
     problem_params['nvars'] = [(129, 129), (65, 65)]  # number of degrees of freedom for each level
     problem_params['comm'] = space_comm  # pass space-communicator to problem class
-    problem_params['sol_tol'] = 1E-12  # set tolerance to PETSc' linear solver
+    problem_params['sol_tol'] = 1E-10  # set tolerance to PETSc' linear solver
 
     # initialize step parameters
     step_params = dict()
@@ -137,6 +137,9 @@ def main():
         out = '   Std and var for number of iterations: %4.2f -- %4.2f' % (float(np.std(niters)), float(np.var(niters)))
         print(out)
 
+        print('   Iteration count linear solver: %i' % P.ksp_itercount)
+        print('   Mean Iteration count per call: %4.2f' % (P.ksp_itercount / max(P.ksp_ncalls, 1)))
+
         timing = sort_stats(filter_stats(stats, type='timing_run'), sortby='time')
 
         out = 'Time to solution: %6.4f sec.' % timing[0][1]

pySDC/projects/TOMS/visualize_pySDC_with_PETSc.py

@@ -98,7 +98,7 @@ def visualize_matrix(result=None):
     plt_helper.newfig(textwidth=120, scale=1.5)
     cmap = plt_helper.plt.get_cmap('RdYlGn_r')
     new_cmap = truncate_colormap(cmap, 0.1, 0.9)
-    plt_helper.plt.imshow(mat, origin='lower', norm=colors.LogNorm(vmin=tmin, vmax=tmax), cmap=new_cmap, aspect='auto')
+    plt_helper.plt.imshow(mat.T, origin='lower', norm=colors.LogNorm(vmin=tmin, vmax=tmax), cmap=new_cmap, aspect='auto')
 
     for key, item in result.items():
         timing = "{:3.1f}".format(item)