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added parallel tests for the compression playground
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‎pySDC/playgrounds/compression/run_parallel_AC_MPIFFT.py‎

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from argparse import ArgumentParser
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import numpy as np
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from mpi4py import MPI
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from pySDC.helpers.stats_helper import filter_stats, sort_stats
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from pySDC.implementations.collocation_classes.gauss_radau_right import CollGaussRadau_Right
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from pySDC.implementations.controller_classes.controller_MPI import controller_MPI
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from pySDC.implementations.sweeper_classes.imex_1st_order import imex_1st_order
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from pySDC.implementations.problem_classes.AllenCahn_MPIFFT import allencahn_imex, allencahn_imex_timeforcing
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from pySDC.implementations.transfer_classes.TransferMesh_MPIFFT import fft_to_fft
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from pySDC.projects.AllenCahn_Bayreuth.AllenCahn_dump import dump
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def run_simulation(name=None, nprocs_space=None):
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"""
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A simple test program to do PFASST runs for the AC equation
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"""
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# set MPI communicator
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comm = MPI.COMM_WORLD
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world_rank = comm.Get_rank()
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world_size = comm.Get_size()
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# split world communicator to create space-communicators
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if nprocs_space is not None:
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color = int(world_rank / nprocs_space)
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else:
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color = int(world_rank / 1)
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space_comm = comm.Split(color=color)
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space_comm.Set_name('Space-Comm')
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space_size = space_comm.Get_size()
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space_rank = space_comm.Get_rank()
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# split world communicator to create time-communicators
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if nprocs_space is not None:
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color = int(world_rank % nprocs_space)
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else:
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color = int(world_rank / world_size)
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time_comm = comm.Split(color=color)
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time_comm.Set_name('Time-Comm')
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time_size = time_comm.Get_size()
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time_rank = time_comm.Get_rank()
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# print(time_size, space_size, world_size)
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# initialize level parameters
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level_params = dict()
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level_params['restol'] = 1E-08
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level_params['dt'] = 1E-03
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level_params['nsweeps'] = [3, 1]
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# initialize sweeper parameters
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sweeper_params = dict()
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sweeper_params['collocation_class'] = CollGaussRadau_Right
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sweeper_params['num_nodes'] = [3]
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sweeper_params['QI'] = ['LU'] # For the IMEX sweeper, the LU-trick can be activated for the implicit part
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sweeper_params['initial_guess'] = 'zero'
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# initialize problem parameters
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problem_params = dict()
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# This defines the number of 'patches' for the simulation per dimension in 2D. L=4 means: 4x4 patches
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problem_params['L'] = 4.0
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# problem_params['L'] = 16.0
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# This defines the number of nodes in space, ideally with about 144 nodes per patch (48 * 12 / 4)
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problem_params['nvars'] = [(48 * 12, 48 * 12), (8 * 12, 8 * 12)]
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# problem_params['nvars'] = [(48 * 48, 48 * 48), (8 * 48, 8 * 48)]
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problem_params['eps'] = [0.04]
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problem_params['radius'] = 0.25
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problem_params['comm'] = space_comm
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problem_params['name'] = name
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problem_params['init_type'] = 'circle_rand'
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problem_params['spectral'] = False
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if name == 'AC-bench-constforce':
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problem_params['dw'] = [-23.59]
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# initialize step parameters
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step_params = dict()
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step_params['maxiter'] = 50
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# initialize controller parameters
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controller_params = dict()
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controller_params['logger_level'] = 20 if space_rank == 0 else 99 # set level depending on rank
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controller_params['predict_type'] = 'fine_only'
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# controller_params['hook_class'] = dump # activate to get data output at each step
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# fill description dictionary for easy step instantiation
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description = dict()
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description['problem_params'] = problem_params # pass problem parameters
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description['sweeper_class'] = imex_1st_order
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description['sweeper_params'] = sweeper_params # pass sweeper parameters
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description['level_params'] = level_params # pass level parameters
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description['step_params'] = step_params # pass step parameters
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description['space_transfer_class'] = fft_to_fft
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if name == 'AC-bench-noforce' or name == 'AC-bench-constforce':
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description['problem_class'] = allencahn_imex
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elif name == 'AC-bench-timeforce':
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description['problem_class'] = allencahn_imex_timeforcing
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else:
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raise NotImplementedError(f'{name} is not implemented')
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# set time parameters
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t0 = 0.0
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Tend = 4 * 0.001
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if space_rank == 0 and time_rank == 0:
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out = f'---------> Running {name} with {time_size} process(es) in time and {space_size} process(es) in space...'
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print(out)
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# instantiate controller
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controller = controller_MPI(controller_params=controller_params, description=description, comm=time_comm)
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# get initial values on finest level
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P = controller.S.levels[0].prob
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uinit = P.u_exact(t0)
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# call main function to get things done...
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uend, stats = controller.run(u0=uinit, t0=t0, Tend=Tend)
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timing = sort_stats(filter_stats(stats, type='timing_setup'), sortby='time')
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max_timing_setup = time_comm.allreduce(timing[0][1], MPI.MAX)
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timing = sort_stats(filter_stats(stats, type='timing_run'), sortby='time')
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max_timing = time_comm.allreduce(timing[0][1], MPI.MAX)
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if space_rank == 0 and time_rank == time_size - 1:
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print()
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out = f'Setup time: {max_timing_setup:.4f} sec.'
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print(out)
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out = f'Time to solution: {max_timing:.4f} sec.'
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print(out)
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iter_counts = sort_stats(filter_stats(stats, type='niter'), sortby='time')
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niters = np.array([item[1] for item in iter_counts])
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out = f'Mean number of iterations: {np.mean(niters):.4f}'
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print(out)
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if __name__ == "__main__":
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# Add parser to get number of processors in space and setup (have to do this here to enable automatic testing)
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# Run this file via `mpirun -np N python run_parallel_AC_MPIFFT.py -n P`,
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# where N is the overall number of processors and P is the number of processors used for spatial parallelization.
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parser = ArgumentParser()
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parser.add_argument("-s", "--setup", help='Specifies the setup', type=str, default='AC-bench-noforce',
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choices=['AC-bench-noforce', 'AC-bench-constforce', 'AC-bench-timeforce'])
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parser.add_argument("-n", "--nprocs_space", help='Specifies the number of processors in space', type=int)
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args = parser.parse_args()
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run_simulation(name=args.setup, nprocs_space=args.nprocs_space)
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‎pySDC/playgrounds/compression/run_parallel_Heat_NumPy.py‎

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from mpi4py import MPI
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from pySDC.helpers.stats_helper import filter_stats, sort_stats
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from pySDC.implementations.controller_classes.controller_MPI import controller_MPI
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from pySDC.implementations.collocation_classes.gauss_radau_right import CollGaussRadau_Right
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from pySDC.implementations.problem_classes.HeatEquation_1D_FD import heat1d
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from pySDC.implementations.sweeper_classes.generic_LU import generic_implicit
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from pySDC.implementations.transfer_classes.TransferMesh import mesh_to_mesh
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def set_parameters_ml():
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"""
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Helper routine to set parameters for the following multi-level runs
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Returns:
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dict: dictionary containing the simulation parameters
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dict: dictionary containing the controller parameters
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float: starting time
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float: end time
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"""
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# initialize level parameters
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level_params = dict()
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level_params['restol'] = 5E-10
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level_params['dt'] = 0.125
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# initialize sweeper parameters
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sweeper_params = dict()
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sweeper_params['collocation_class'] = CollGaussRadau_Right
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sweeper_params['QI'] = 'LU'
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sweeper_params['num_nodes'] = [3]
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# initialize problem parameters
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problem_params = dict()
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problem_params['nu'] = 0.1 # diffusion coefficient
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problem_params['freq'] = 2 # frequency for the test value
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problem_params['nvars'] = [63, 31] # number of degrees of freedom for each level
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# initialize step parameters
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step_params = dict()
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step_params['maxiter'] = 50
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step_params['errtol'] = 1E-05
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# initialize space transfer parameters
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space_transfer_params = dict()
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space_transfer_params['rorder'] = 2
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space_transfer_params['iorder'] = 6
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# initialize controller parameters
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controller_params = dict()
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controller_params['logger_level'] = 30
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controller_params['all_to_done'] = True # can ask the controller to keep iterating all steps until the end
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controller_params['use_iteration_estimator'] = False # activate iteration estimator
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# fill description dictionary for easy step instantiation
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description = dict()
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description['problem_class'] = heat1d # pass problem class
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description['problem_params'] = problem_params # pass problem parameters
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description['sweeper_class'] = generic_implicit # pass sweeper
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description['sweeper_params'] = sweeper_params # pass sweeper parameters
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description['level_params'] = level_params # pass level parameters
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description['step_params'] = step_params # pass step parameters
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description['space_transfer_class'] = mesh_to_mesh # pass spatial transfer class
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description['space_transfer_params'] = space_transfer_params # pass paramters for spatial transfer
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# set time parameters
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t0 = 0.0
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Tend = 1.0
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return description, controller_params, t0, Tend
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if __name__ == "__main__":
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"""
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A simple test program to do MPI-parallel PFASST runs
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"""
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# set MPI communicator
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comm = MPI.COMM_WORLD
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# get parameters from Part A
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description, controller_params, t0, Tend = set_parameters_ml()
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# instantiate controllers
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controller = controller_MPI(controller_params=controller_params, description=description, comm=comm)
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# get initial values on finest level
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P = controller.S.levels[0].prob
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uinit = P.u_exact(t0)
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# call main functions to get things done...
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uend, stats = controller.run(u0=uinit, t0=t0, Tend=Tend)
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# filter statistics by type (number of iterations)
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filtered_stats = filter_stats(stats, type='niter')
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# convert filtered statistics to list of iterations count, sorted by process
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iter_counts = sort_stats(filtered_stats, sortby='time')
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# combine statistics into list of statistics
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iter_counts_list = comm.gather(iter_counts, root=0)
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rank = comm.Get_rank()
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size = comm.Get_size()
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if rank == 0:
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out = 'Working with %2i processes...' % size
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print(out)
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# compute exact solutions and compare with both results
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uex = P.u_exact(Tend)
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err = abs(uex - uend)
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out = 'Error vs. exact solution: %12.8e' % err
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print(out)
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# build one list of statistics instead of list of lists, the sort by time
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iter_counts_gather = [item for sublist in iter_counts_list for item in sublist]
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iter_counts = sorted(iter_counts_gather, key=lambda tup: tup[0])
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# compute and print statistics
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for item in iter_counts:
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out = 'Number of iterations for time %4.2f: %1i ' % (item[0], item[1])
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print(out)

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