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)
# Execute!
solver.run()
# Finalise MPI
MPI.Finalize()
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__():
if hasattr(os, 'fork'):
from pytools.prefork import enable_prefork
enable_prefork()
# Define MPI communication world
from mpi4py import MPI
MPI.Init()
# define the local rank based cuda device
print("Local rank", get_local_rank())
os.environ.pop('CUDA_DEVICE', None)
devid = get_local_rank()
os.environ['CUDA_DEVICE'] = str(devid)
# CUDA device number (used by pycuda.autoinit)
#from pycuda.autoinit import context
#import pycuda.autoinit
cuda.init()
cudadevice = cuda.Device(devid)
cudacontext = cudadevice.make_context()
import atexit
atexit.register(cudacontext.pop)
# define the main process
main()
# finalize everything
MPI.Finalize()
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(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():
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()
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()
__author__ = "W.R.Saunders"
__copyright__ = "Copyright 2016, W.R.Saunders"
__license__ = "GPL"
import mpi4py
mpi4py.rc.initialize = False
mpi4py.rc.finalize = True
from mpi4py import MPI as _MPI
_is_init = _MPI.Is_initialized()
if _is_init:
print("Warning MPI was initialised before prefork, this is not supported with OpenMPI.")
from pytools import prefork
prefork.enable_prefork()
__all__ = [ 'mpi',
'access',
'cell',
'data',
'domain',
'halo',
'host',
'kernel',
'loop',
'method',
'pairloop',
'pio',
'runtime',
'state',
