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镜像自地址 https://gitee.com/sunhf/gprMax.git 已同步 2025-08-07 04:56:51 +08:00
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Removed mpi_runs function as the MPI code needs to be slightly different for a normal simulation and a Taguchi optimisation.

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Craig Warren
2015-12-15 12:07:27 +00:00
父节点 7e84b841ea
当前提交 26c123f1b1
共有 1 个文件被更改，包括 118 次插入 和 88 次删除
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206
gprMax/gprMax.py
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@@ -57,9 +57,9 @@ def main():
# Parse command line arguments
parser = argparse.ArgumentParser(prog='gprMax', description='Electromagnetic modelling software based on the Finite-Difference Time-Domain (FDTD) method')
parser.add_argument('inputfile', help='path to and name of inputfile')
parser.add_argument('--geometry-only', action='store_true', default=False, help='only build model and produce geometry files')
parser.add_argument('-n', default=1, type=int, help='number of times to run the input file')
parser.add_argument('-mpi', action='store_true', default=False, help='switch on MPI')
parser.add_argument('--geometry-only', action='store_true', default=False, help='only build model and produce geometry file(s)')
parser.add_argument('--write-python', action='store_true', default=False, help='write an input file after any Python code blocks in the original input file have been processed')
parser.add_argument('--opt-taguchi', action='store_true', default=False, help='optimise parameters using the Taguchi optimisation method')
args = parser.parse_args()
@@ -72,14 +72,11 @@ def main():
if args.opt_taguchi and numbermodelruns > 1:
raise CmdInputError('When a Taguchi optimisation is being carried out the number of model runs argument is not required')
print('Model input file: {}\n'.format(inputfile))
#############################################
# Main routine for Taguchi optimisation #
#############################################
if args.opt_taguchi:
from user_libs.optimisations.taguchi import select_OA, calculate_ranges_experiments, calculate_optimal_levels, fitness_max
######## These should be read from #opt_taguchi block from input file
@@ -91,6 +88,9 @@ def main():
optparams['rickeramp'] = [0.25, 5]
# optparams['sig'] = [0.001, 0.1]
# Store initial parameter ranges
optparamsinit = list(optparams.items())
# Dictionary to hold history of optmised values of parameters
optparamshist = OrderedDict((key, list()) for key in optparams)
@@ -120,11 +120,65 @@ def main():
usernamespace['number_model_runs'] = numbermodelruns
# Set parameter ranges and define experiments
optparams, levels, levelsdiff = calculate_ranges_experiments(optparams, levels, levelsopt, levelsdiff, OA, N, k, s, i)
optparams, levels, levelsdiff = calculate_ranges_experiments(optparams, optparamsinit, levels, levelsopt, levelsdiff, OA, N, k, s, i)
# Mixed mode MPI/OpenMP - task farm for model runs with MPI; each model parallelised with OpenMP
if args.mpi:
mpi_runs(args, numbermodelruns, inputfile, usernamespace)
from mpi4py import MPI
# Define MPI message tags
tags = Enum('tags', {'READY': 0, 'DONE': 1, 'EXIT': 2, 'START': 3})
# Initializations and preliminaries
comm = MPI.COMM_WORLD # get MPI communicator object
size = comm.size # total number of processes
rank = comm.rank # rank of this process
status = MPI.Status() # get MPI status object
name = MPI.Get_processor_name() # get name of processor/host
if rank == 0:
# Master process
modelrun = 1
numworkers = size - 1
closedworkers = 0
print('Master: PID {} on {} using {} workers.'.format(os.getpid(), name, numworkers))
while closedworkers < numworkers:
data = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)
source = status.Get_source()
tag = status.Get_tag()
if tag == tags.READY.value:
# Worker is ready, so send it a task
if modelrun < numbermodelruns + 1:
comm.send(modelrun, dest=source, tag=tags.START.value)
print('Master: sending model {} to worker {}.'.format(modelrun, source))
modelrun += 1
else:
comm.send(None, dest=source, tag=tags.EXIT.value)
elif tag == tags.DONE.value:
print('Worker {}: completed.'.format(source))
elif tag == tags.EXIT.value:
print('Worker {}: exited.'.format(source))
closedworkers += 1
else:
# Worker process
print('Worker {}: PID {} on {} requesting {} OpenMP threads.'.format(rank, os.getpid(), name, os.environ.get('OMP_NUM_THREADS')))
while True:
comm.send(None, dest=0, tag=tags.READY.value)
# Receive a model number to run from the master
modelrun = comm.recv(source=0, tag=MPI.ANY_TAG, status=status)
tag = status.Get_tag()
if tag == tags.START.value:
# Run a model
# Add specific value for each parameter to optimise, for each experiment to user accessible namespace
optnamespace = usernamespace.copy()
optnamespace.update((key, value[modelrun - 1]) for key, value in optparams.items())
run_model(args, modelrun, numbermodelruns, inputfile, usernamespace)
comm.send(None, dest=0, tag=tags.DONE.value)
elif tag == tags.EXIT.value:
break
comm.send(None, dest=0, tag=tags.EXIT.value)
# Standard behaviour - models run serially; each model parallelised with OpenMP
else:
@@ -136,21 +190,17 @@ def main():
run_model(args, modelrun, numbermodelruns, inputfile, optnamespace)
tsimend = perf_counter()
print('\nTotal simulation time [HH:MM:SS]: {}'.format(datetime.timedelta(seconds=int(tsimend - tsimstart))))
print('\nTaguchi optimisation, iteration {}, completed initial {} experiments.'.format(i + 1, numbermodelruns))
# Calculate fitness metric for each experiment
for exp in range(1, numbermodelruns + 1):
inputfileparts = os.path.splitext(inputfile)
outputfile = inputfileparts[0] + str(exp) + '.out'
fitness[exp - 1] = fitness_max(outputfile, ['myRx'])
print(fitness)
print('\nTaguchi optimisation, iteration {}, completed calculation of fitness of experiments.'.format(i + 1))
print('\nTaguchi optimisation, iteration {}: completed initial {} experiments completed with fitness values {}.'.format(i + 1, numbermodelruns, fitness))
# Calculate optimal levels from results of fitness metric by building a response table and update dictionary of parameters with optimal values
optparams, levelsopt = calculate_optimal_levels(optparams, levels, levelsopt, fitness, OA, N, k)
print(optparams)
# Run a confirmation experiment
numbermodelruns = 1
@@ -166,19 +216,14 @@ def main():
tsimend = perf_counter()
print('\nTotal simulation time [HH:MM:SS]: {}'.format(datetime.timedelta(seconds=int(tsimend - tsimstart))))
print(optparamshist)
print('\nTaguchi optimisation, iteration {}, completed confirmation experiment.'.format(i + 1))
# Calculate fitness metric for confirmation experiment
inputfileparts = os.path.splitext(inputfile)
outputfile = inputfileparts[0] + '.out'
fitnesshist[i] = fitness_max(outputfile, ['myRx'])
print(fitnesshist)
# Stop if fitness criteria have been met
print('\nTaguchi optimisation, iteration {} completed.\n{}\n'.format(i + 1, 65*'*'))
print('\nTaguchi optimisation, iteration {} completed with optimal values {} and fitness value {}\n{}\n'.format(i + 1, optparams, fitnesshist[i], 65*'*'))
i += 1
@@ -191,7 +236,58 @@ def main():
# Mixed mode MPI/OpenMP - task farm for model runs with MPI; each model parallelised with OpenMP
if args.mpi:
mpi_runs(args, numbermodelruns, inputfile, usernamespace)
from mpi4py import MPI
# Define MPI message tags
tags = Enum('tags', {'READY': 0, 'DONE': 1, 'EXIT': 2, 'START': 3})
# Initializations and preliminaries
comm = MPI.COMM_WORLD # get MPI communicator object
size = comm.size # total number of processes
rank = comm.rank # rank of this process
status = MPI.Status() # get MPI status object
name = MPI.Get_processor_name() # get name of processor/host
if rank == 0:
# Master process
modelrun = 1
numworkers = size - 1
closedworkers = 0
print('Master: PID {} on {} using {} workers.'.format(os.getpid(), name, numworkers))
while closedworkers < numworkers:
data = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)
source = status.Get_source()
tag = status.Get_tag()
if tag == tags.READY.value:
# Worker is ready, so send it a task
if modelrun < numbermodelruns + 1:
comm.send(modelrun, dest=source, tag=tags.START.value)
print('Master: sending model {} to worker {}.'.format(modelrun, source))
modelrun += 1
else:
comm.send(None, dest=source, tag=tags.EXIT.value)
elif tag == tags.DONE.value:
print('Worker {}: completed.'.format(source))
elif tag == tags.EXIT.value:
print('Worker {}: exited.'.format(source))
closedworkers += 1
else:
# Worker process
print('Worker {}: PID {} on {} requesting {} OpenMP threads.'.format(rank, os.getpid(), name, os.environ.get('OMP_NUM_THREADS')))
while True:
comm.send(None, dest=0, tag=tags.READY.value)
# Receive a model number to run from the master
modelrun = comm.recv(source=0, tag=MPI.ANY_TAG, status=status)
tag = status.Get_tag()
if tag == tags.START.value:
# Run a model
run_model(args, modelrun, numbermodelruns, inputfile, usernamespace)
comm.send(None, dest=0, tag=tags.DONE.value)
elif tag == tags.EXIT.value:
break
comm.send(None, dest=0, tag=tags.EXIT.value)
# Standard behaviour - models run serially; each model parallelised with OpenMP
else:
@@ -215,6 +311,8 @@ def run_model(args, modelrun, numbermodelruns, inputfile, usernamespace):
usernamespace (dict): Namespace that can be accessed by user in any Python code blocks in input file.
"""
print('Model input file: {}\n'.format(inputfile))
# Add the current model run to namespace that can be accessed by user in any Python code blocks in input file
usernamespace['current_model_run'] = modelrun
print('Constants/variables available for Python scripting: {}\n'.format(usernamespace))
@@ -464,71 +562,3 @@ def run_model(args, modelrun, numbermodelruns, inputfile, usernamespace):
print('\n\nSolving took [HH:MM:SS]: {}'.format(datetime.timedelta(seconds=int(tsolveend - tsolvestart))))
if sys.platform != 'win32':
print('Peak memory (approx) used: {}'.format(human_size(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss, False)))
def mpi_runs(args, numbermodelruns, inputfile, usernamespace):
"""Runs models in MPI environment - processes the input file; builds the Yee cells; calculates update coefficients; runs main FDTD loop.
Args:
args (dict): Namespace with command line arguments
numbermodelruns (int): Total number of model runs.
inputfile (str): Name of the input file to open.
usernamespace (dict): Namespace that can be accessed by user in any Python code blocks in input file.
"""
from mpi4py import MPI
# Define MPI message tags
tags = Enum('tags', {'READY': 0, 'DONE': 1, 'EXIT': 2, 'START': 3})
# Initializations and preliminaries
comm = MPI.COMM_WORLD # get MPI communicator object
size = comm.size # total number of processes
rank = comm.rank # rank of this process
status = MPI.Status() # get MPI status object
name = MPI.Get_processor_name() # get name of processor/host
if rank == 0:
# Master process
modelrun = 1
numworkers = size - 1
closedworkers = 0
print('Master: PID {} on {} using {} workers.'.format(os.getpid(), name, numworkers))
while closedworkers < numworkers:
data = comm.recv(source=MPI.ANY_SOURCE, tag=MPI.ANY_TAG, status=status)
source = status.Get_source()
tag = status.Get_tag()
if tag == tags.READY.value:
# Worker is ready, so send it a task
if modelrun < numbermodelruns + 1:
comm.send(modelrun, dest=source, tag=tags.START.value)
print('Master: sending model {} to worker {}.'.format(modelrun, source))
modelrun += 1
else:
comm.send(None, dest=source, tag=tags.EXIT.value)
elif tag == tags.DONE.value:
print('Worker {}: completed.'.format(source))
elif tag == tags.EXIT.value:
print('Worker {}: exited.'.format(source))
closedworkers += 1
else:
# Worker process
print('Worker {}: PID {} on {} requesting {} OpenMP threads.'.format(rank, os.getpid(), name, os.environ.get('OMP_NUM_THREADS')))
while True:
comm.send(None, dest=0, tag=tags.READY.value)
# Receive a model number to run from the master
modelrun = comm.recv(source=0, tag=MPI.ANY_TAG, status=status)
tag = status.Get_tag()
if tag == tags.START.value:
# Run a model
run_model(args, modelrun, numbermodelruns, inputfile, usernamespace)
comm.send(None, dest=0, tag=tags.DONE.value)
elif tag == tags.EXIT.value:
break
comm.send(None, dest=0, tag=tags.EXIT.value)