def _process_common(args, mesh, soln, cfg):
# Prefork to allow us to exec processes after MPI is initialised
if hasattr(os, 'fork'):
from pytools.prefork import enable_prefork
enable_prefork()
# Import but do not initialise MPI
from mpi4py import MPI
# Manually initialise MPI
MPI.Init()
# Ensure MPI is suitably cleaned up
register_finalize_handler()
# Create a backend
backend = get_backend(args.backend, cfg)
# Get the mapping from physical ranks to MPI ranks
rallocs = get_rank_allocation(mesh, cfg)
# Construct the solver
solver = get_solver(backend, rallocs, mesh, soln, cfg)
# If we are running interactively then create a progress bar
if args.progress and MPI.COMM_WORLD.rank == 0:
pb = ProgressBar(solver.tstart, solver.tcurr, solver.tend)
# Register a callback to update the bar after each step
callb = lambda intg: pb.advance_to(intg.tcurr)
solver.completed_step_handlers.append(callb)
# Execute!
solver.run()
def _process_common(args, mesh, soln, cfg):
# Prefork to allow us to exec processes after MPI is initialised
if hasattr(os, 'fork'):
from pytools.prefork import enable_prefork
enable_prefork()
# Import but do not initialise MPI
from mpi4py import MPI
# Manually initialise MPI
MPI.Init()
# Ensure MPI is suitably cleaned up
register_finalize_handler()
# Create a backend
backend = get_backend(args.backend, cfg)
# Get the mapping from physical ranks to MPI ranks
rallocs = get_rank_allocation(mesh, cfg)
# Construct the solver
solver = get_solver(backend, rallocs, mesh, soln, cfg)
# If we are running interactively then create a progress bar
if args.progress and MPI.COMM_WORLD.rank == 0:
pb = ProgressBar(solver.tstart, solver.tcurr, solver.tend)
# Register a callback to update the bar after each step
callb = lambda intg: pb.advance_to(intg.tcurr)
solver.completed_step_handlers.append(callb)
# Execute!
solver.run()
def main():
from mpi4py import MPI
ap = ArgumentParser(prog="pyfr-sim", description="Runs a PyFR simulation")
ap.add_argument("--verbose", "-v", action="count")
ap.add_argument("--backend", "-b", default="cuda", help="Backend to use")
ap.add_argument("--progress", "-p", action="store_true", help="show a progress bar")
ap.add_argument("--nansweep", "-n", metavar="N", type=int, help="check for NaNs every N steps")
sp = ap.add_subparsers(help="sub-command help")
ap_run = sp.add_parser("run", help="run --help")
ap_run.add_argument("mesh", help="mesh file")
ap_run.add_argument("cfg", type=FileType("r"), help="config file")
ap_run.set_defaults(process=process_run)
ap_restart = sp.add_parser("restart", help="restart --help")
ap_restart.add_argument("mesh", help="mesh file")
ap_restart.add_argument("soln", help="solution file")
ap_restart.add_argument("cfg", nargs="?", type=FileType("r"), help="new config file")
ap_restart.set_defaults(process=process_restart)
# Parse the arguments
args = ap.parse_args()
mesh, soln, cfg = args.process(args)
# Ensure MPI is suitably cleaned up
register_finalize_handler()
# Create a backend
backend = get_backend(args.backend, cfg)
# Get the mapping from physical ranks to MPI ranks
rallocs = get_rank_allocation(mesh, cfg)
# Construct the solver
solver = get_solver(backend, rallocs, mesh, soln, cfg)
# If we are running interactively then create a progress bar
if args.progress and MPI.COMM_WORLD.rank == 0:
pb = ProgressBar(solver.tstart, solver.tcurr, solver.tend)
# Register a callback to update the bar after each step
callb = lambda intg: pb.advance_to(intg.tcurr)
solver.completed_step_handlers.append(callb)
# NaN sweeping
if args.nansweep:
def nansweep(intg):
if intg.nsteps % args.nansweep == 0:
if any(np.isnan(np.sum(s)) for s in intg.soln):
raise RuntimeError("NaNs detected at t = {}".format(intg.tcurr))
solver.completed_step_handlers.append(nansweep)
# Execute!
solver.run()
def _process_common(self, args, mesh, soln, cfg):
# Prefork to allow us to exec processes after MPI is initialised
if hasattr(os, 'fork'):
from pytools.prefork import enable_prefork
enable_prefork()
# Ensure MPI is suitably cleaned up
register_finalize_handler()
# Create a backend
backend = get_backend(args.backend, cfg)
# Get the mapping from physical ranks to MPI ranks
rallocs = get_rank_allocation(mesh, cfg)
# Construct the solver
self.solver = get_solver(backend, rallocs, mesh, soln, cfg)
def main():
from mpi4py import MPI
ap = ArgumentParser(prog='pyfr-sim', description='Runs a PyFR simulation')
ap.add_argument('--verbose', '-v', action='count')
ap.add_argument('--backend', '-b', default='cuda', help='Backend to use')
ap.add_argument('--progress',
'-p',
action='store_true',
help='show a progress bar')
ap.add_argument('--nansweep',
'-n',
metavar='N',
type=int,
help='check for NaNs every N steps')
sp = ap.add_subparsers(help='sub-command help')
ap_run = sp.add_parser('run', help='run --help')
ap_run.add_argument('mesh', help='mesh file')
ap_run.add_argument('cfg', type=FileType('r'), help='config file')
ap_run.set_defaults(process=process_run)
ap_restart = sp.add_parser('restart', help='restart --help')
ap_restart.add_argument('mesh', help='mesh file')
ap_restart.add_argument('soln', help='solution file')
ap_restart.add_argument('cfg',
nargs='?',
type=FileType('r'),
help='new config file')
ap_restart.set_defaults(process=process_restart)
# Parse the arguments
args = ap.parse_args()
mesh, soln, cfg = args.process(args)
# Ensure MPI is suitably cleaned up
register_finalize_handler()
# Create a backend
backend = get_backend(args.backend, cfg)
# Get the mapping from physical ranks to MPI ranks
rallocs = get_rank_allocation(mesh, cfg)
# Construct the solver
solver = get_solver(backend, rallocs, mesh, soln, cfg)
# If we are running interactively then create a progress bar
if args.progress and MPI.COMM_WORLD.rank == 0:
pb = ProgressBar(solver.tstart, solver.tcurr, solver.tend)
# Register a callback to update the bar after each step
callb = lambda intg: pb.advance_to(intg.tcurr)
solver.completed_step_handlers.append(callb)
# NaN sweeping
if args.nansweep:
def nansweep(intg):
if intg.nsteps % args.nansweep == 0:
if any(np.isnan(np.sum(s)) for s in intg.soln):
raise RuntimeError('NaNs detected at t = {}'.format(
intg.tcurr))
solver.completed_step_handlers.append(nansweep)
# Execute!
solver.run()
def main():
ap = ArgumentParser(prog='pyfr-sim', description='Runs a PyFR simulation')
ap.add_argument('--verbose', '-v', action='count')
ap.add_argument('--backend', '-b', default='cuda', help='Backend to use')
ap.add_argument('--progress', '-p', action='store_true',
help='show a progress bar')
ap.add_argument('--nansweep', '-n', metavar='N', type=int,
help='check for NaNs every N steps')
sp = ap.add_subparsers(help='sub-command help')
ap_run = sp.add_parser('run', help='run --help')
ap_run.add_argument('mesh', help='mesh file')
ap_run.add_argument('cfg', type=FileType('r'), help='config file')
ap_run.set_defaults(process=process_run)
ap_restart = sp.add_parser('restart', help='restart --help')
ap_restart.add_argument('mesh', help='mesh file')
ap_restart.add_argument('soln', help='solution file')
ap_restart.add_argument('cfg', nargs='?', type=FileType('r'),
help='new config file')
ap_restart.set_defaults(process=process_restart)
# Parse the arguments
args = ap.parse_args()
mesh, soln, cfg = args.process(args)
# Prefork to allow us to exec processes after MPI is initialised
if hasattr(os, 'fork'):
from pytools.prefork import enable_prefork
enable_prefork()
# Import and hence initialise MPI
from mpi4py import MPI
# Ensure MPI is suitably cleaned up
register_finalize_handler()
# Create a backend
backend = get_backend(args.backend, cfg)
# Get the mapping from physical ranks to MPI ranks
rallocs = get_rank_allocation(mesh, cfg)
# Construct the solver
solver = get_solver(backend, rallocs, mesh, soln, cfg)
# If we are running interactively then create a progress bar
if args.progress and MPI.COMM_WORLD.rank == 0:
pb = ProgressBar(solver.tstart, solver.tcurr, solver.tend)
# Register a callback to update the bar after each step
callb = lambda intg: pb.advance_to(intg.tcurr)
solver.completed_step_handlers.append(callb)
# NaN sweeping
if args.nansweep:
def nansweep(intg):
if intg.nsteps % args.nansweep == 0:
if any(np.isnan(np.sum(s)) for s in intg.soln):
raise RuntimeError('NaNs detected at t = {}'
.format(intg.tcurr))
solver.completed_step_handlers.append(nansweep)
# Execute!
solver.run()
