[demos] parallelize burgers_ei demo

src/pymordemos/burgers_ei.py

@@ -70,6 +70,12 @@
   --vx=XSPEED                     Speed in x-direction [default: 1].
 
   --vy=YSPEED                     Speed in y-direction [default: 1].
+
+  --ipython-engines=COUNT         If positive, the number of IPython cluster engines to use
+                                  for parallel greedy search. If zero, no parallelization
+                                  is performed. [default: 0]
+
+  --ipython-profile=PROFILE       IPython profile to use for parallelization.
 """
 
 from __future__ import absolute_import, division, print_function
@@ -90,6 +96,7 @@
 from pymor.domaindiscretizers.default import discretize_domain_default
 from pymor.grids.rect import RectGrid
 from pymor.grids.tria import TriaGrid
+from pymor.parallel.default import new_parallel_pool
 from pymor.reductors.basic import reduce_generic_rb, reduce_to_subbasis
 from pymor.vectorarrays.numpy import NumpyVectorArray
 
@@ -111,6 +118,7 @@ def burgers_demo(args):
     args['--test'] = int(args['--test'])
     args['--vx'] = float(args['--vx'])
     args['--vy'] = float(args['--vy'])
+    args['--ipython-engines'] = int(args['--ipython-engines'])
     args['EXP_MIN'] = int(args['EXP_MIN'])
     args['EXP_MAX'] = int(args['EXP_MAX'])
     args['EI_SNAPSHOTS'] = int(args['EI_SNAPSHOTS'])
@@ -150,12 +158,14 @@ def burgers_demo(args):
             legend = legend + ('exponent: {}'.format(mu['exponent']),)
         discretization.visualize(Us, legend=legend, title='Detailed Solutions', block=True)
 
+    pool = new_parallel_pool(ipython_num_engines=args['--ipython-engines'], ipython_profile=args['--ipython-profile'])
     ei_discretization, ei_data = interpolate_operators(discretization, ['operator'],
                                                        discretization.parameter_space.sample_uniformly(args['EI_SNAPSHOTS']),  # NOQA
                                                        error_norm=discretization.l2_norm,
                                                        target_error=1e-10,
                                                        max_interpolation_dofs=args['EISIZE'],
-                                                       product=discretization.l2_product)
+                                                       product=discretization.l2_product,
+                                                       pool=pool)
 
     if args['--plot-ei-err']:
         print('Showing some EI errors')
@@ -188,7 +198,8 @@ def reductor(discretization, rb, extends=None):
 
     greedy_data = greedy(discretization, reductor, discretization.parameter_space.sample_uniformly(args['SNAPSHOTS']),
                          use_estimator=False, error_norm=lambda U: np.max(discretization.l2_norm(U)),
-                         extension_algorithm=extension_algorithm, max_extensions=args['RBSIZE'])
+                         extension_algorithm=extension_algorithm, max_extensions=args['RBSIZE'],
+                         pool=pool)
 
     rb_discretization, reconstructor = greedy_data['reduced_discretization'], greedy_data['reconstructor']