def process(workflow, inputs, args):
processes = {}
inputmappings = {}
outputmappings = {}
success = True
nodes = [node.getContainedObject() for node in workflow.graph.nodes()]
if rank == 0 and not args.simple:
try:
processes, inputmappings, outputmappings =\
processor.assign_and_connect(workflow, size)
except:
success = False
success = comm.bcast(success, root=0)
if args.simple or not success:
ubergraph = processor.create_partitioned(workflow)
nodes = [node.getContainedObject() for node in ubergraph.graph.nodes()]
if rank == 0:
print('Partitions: %s' % ', '.join(
('[%s]' % ', '.join((pe.id for pe in part))
for part in workflow.partitions)))
for node in ubergraph.graph.nodes():
wrapperPE = node.getContainedObject()
ns = [n.getContainedObject().id
for n in wrapperPE.workflow.graph.nodes()]
print('%s contains %s' % (wrapperPE.id, ns))
try:
processes, inputmappings, outputmappings = \
processor.assign_and_connect(ubergraph, size)
inputs = processor.map_inputs_to_partitions(ubergraph, inputs)
success = True
except:
print('dispel4py.mpi_process: '
'Not enough processes for execution of graph')
success = False
success = comm.bcast(success, root=0)
if not success:
return
inputs = {pe.id: v for pe, v in inputs.items()}
processes = comm.bcast(processes, root=0)
inputmappings = comm.bcast(inputmappings, root=0)
outputmappings = comm.bcast(outputmappings, root=0)
inputs = comm.bcast(inputs, root=0)
if rank == 0:
print('Processes: %s' % processes)
# print('Inputs: %s' % inputs)
for pe in nodes:
if rank in processes[pe.id]:
provided_inputs = processor.get_inputs(pe, inputs)
wrapper = MPIWrapper(pe, provided_inputs)
wrapper.targets = outputmappings[rank]
wrapper.sources = inputmappings[rank]
wrapper.process()
def process(workflow, inputs, args):
processes = {}
inputmappings = {}
outputmappings = {}
success = True
nodes = [node.getContainedObject() for node in workflow.graph.nodes()]
if rank == 0 and not args.simple:
try:
processes, inputmappings, outputmappings = processor.assign_and_connect(workflow, size)
except:
success = False
success = comm.bcast(success, root=0)
if args.simple or not success:
ubergraph = processor.create_partitioned(workflow)
nodes = [node.getContainedObject() for node in ubergraph.graph.nodes()]
if rank == 0:
print(
"Partitions: %s"
% ", ".join(("[%s]" % ", ".join((pe.id for pe in part)) for part in workflow.partitions))
)
for node in ubergraph.graph.nodes():
wrapperPE = node.getContainedObject()
ns = [n.getContainedObject().id for n in wrapperPE.workflow.graph.nodes()]
print("%s contains %s" % (wrapperPE.id, ns))
try:
processes, inputmappings, outputmappings = processor.assign_and_connect(ubergraph, size)
inputs = processor.map_inputs_to_partitions(ubergraph, inputs)
success = True
except:
print("dispel4py.mpi_process: " "Not enough processes for execution of graph")
success = False
success = comm.bcast(success, root=0)
if not success:
return
inputs = {pe.id: v for pe, v in inputs.items()}
processes = comm.bcast(processes, root=0)
inputmappings = comm.bcast(inputmappings, root=0)
outputmappings = comm.bcast(outputmappings, root=0)
inputs = comm.bcast(inputs, root=0)
if rank == 0:
print("Processes: %s" % processes)
# print('Inputs: %s' % inputs)
for pe in nodes:
if rank in processes[pe.id]:
provided_inputs = processor.get_inputs(pe, inputs)
wrapper = MPIWrapper(pe, provided_inputs)
wrapper.targets = outputmappings[rank]
wrapper.sources = inputmappings[rank]
wrapper.process()
def process(workflow, inputs, args):
size = args.num
success = True
nodes = [node.getContainedObject() for node in workflow.graph.nodes()]
if not args.simple:
try:
result = processor.assign_and_connect(workflow, size)
processes, inputmappings, outputmappings = result
except:
success = False
if args.simple or not success:
ubergraph = processor.create_partitioned(workflow)
print('Partitions: %s' % ', '.join(('[%s]' % ', '.join(
(pe.id for pe in part)) for part in workflow.partitions)))
for node in ubergraph.graph.nodes():
wrapperPE = node.getContainedObject()
pes = [n.getContainedObject().id for
n in wrapperPE.workflow.graph.nodes()]
print('%s contains %s' % (wrapperPE.id, pes))
try:
result = processor.assign_and_connect(ubergraph, size)
if result is None:
return 'dispel4py.multi_process: ' \
'Not enough processes for execution of graph'
processes, inputmappings, outputmappings = result
inputs = processor.map_inputs_to_partitions(ubergraph, inputs)
success = True
nodes = [node.getContainedObject()
for node in ubergraph.graph.nodes()]
except:
print(traceback.format_exc())
return 'dispel4py.multi_process: ' \
'Could not create mapping for execution of graph'
print('Processes: %s' % processes)
process_pes = {}
queues = {}
result_queue = None
try:
if args.results:
result_queue = multiprocessing.Queue()
except AttributeError:
pass
for pe in nodes:
provided_inputs = processor.get_inputs(pe, inputs)
for proc in processes[pe.id]:
cp = copy.deepcopy(pe)
cp.rank = proc
cp.log = types.MethodType(simpleLogger, cp)
wrapper = MultiProcessingWrapper(proc, cp, provided_inputs)
process_pes[proc] = wrapper
wrapper.input_queue = multiprocessing.Queue()
wrapper.input_queue.name = 'Queue_%s_%s' % (cp.id, cp.rank)
wrapper.result_queue = result_queue
queues[proc] = wrapper.input_queue
wrapper.targets = outputmappings[proc]
wrapper.sources = inputmappings[proc]
for proc in process_pes:
wrapper = process_pes[proc]
wrapper.output_queues = {}
for target in wrapper.targets.values():
for inp, comm in target:
for i in comm.destinations:
wrapper.output_queues[i] = queues[i]
jobs = []
for wrapper in process_pes.values():
p = multiprocessing.Process(target=_processWorker, args=(wrapper,))
jobs.append(p)
for j in jobs:
j.start()
for j in jobs:
j.join()
if result_queue:
result_queue.put(STATUS_TERMINATED)
return result_queue
def process(workflow, inputs, args):
processes = {}
inputmappings = {}
outputmappings = {}
success = True
nodes = [node.getContainedObject() for node in workflow.graph.nodes()]
if rank == 0 and not args.simple:
try:
processes, inputmappings, outputmappings =\
processor.assign_and_connect(workflow, size)
except:
success = False
success = comm.bcast(success, root=0)
if args.simple or not success:
ubergraph = processor.create_partitioned(workflow)
nodes = [node.getContainedObject() for node in ubergraph.graph.nodes()]
if rank == 0:
print('Partitions: %s' % ', '.join(('[%s]' % ', '.join(
(pe.id for pe in part)) for part in workflow.partitions)))
for node in ubergraph.graph.nodes():
wrapperPE = node.getContainedObject()
print('%s contains %s' % (wrapperPE.id,
[n.getContainedObject().id for n in
wrapperPE.workflow.graph.nodes()]))
try:
processes, inputmappings, outputmappings =\
processor.assign_and_connect(ubergraph, size)
inputs = processor.map_inputs_to_partitions(ubergraph, inputs)
success = True
except:
# print traceback.format_exc()
print('dispel4py.mpi_process: \
Not enough processes for execution of graph')
success = False
success = comm.bcast(success, root=0)
if not success:
return
try:
inputs = {pe.id: v for pe, v in inputs.items()}
except AttributeError:
pass
processes = comm.bcast(processes, root=0)
inputmappings = comm.bcast(inputmappings, root=0)
outputmappings = comm.bcast(outputmappings, root=0)
inputs = comm.bcast(inputs, root=0)
if rank == 0:
print('Processes: %s' % processes)
# print 'Inputs: %s' % inputs
for pe in nodes:
if rank in processes[pe.id]:
provided_inputs = processor.get_inputs(pe, inputs)
wrapper = MPIWrapper(pe, provided_inputs)
wrapper.targets = outputmappings[rank]
wrapper.sources = inputmappings[rank]
wrapper.process()
def process(workflow, inputs, args):
workflow_submission_id = uuid.uuid1().hex
print workflow_submission_id
# Check if switch profile mode on
if args.profileOn:
manager = multiprocessing.Manager()
# A dict to store characterization
profiles = manager.dict()
multi_monitor = Monitor(profiles, args, workflow)
t1 = time.time()
size = args.num
success = True
nodes = [node.getContainedObject() for node in workflow.graph.nodes()]
if not args.simple:
try:
result = processor.assign_and_connect(workflow, size)
processes, inputmappings, outputmappings = result
except:
success = False
if args.simple or not success:
ubergraph = processor.create_partitioned(workflow)
print('Partitions: %s' % ', '.join(('[%s]' % ', '.join(
(pe.id for pe in part)) for part in workflow.partitions)))
for node in ubergraph.graph.nodes():
wrapperPE = node.getContainedObject()
pes = [n.getContainedObject().id for
n in wrapperPE.workflow.graph.nodes()]
print('%s contains %s' % (wrapperPE.id, pes))
try:
result = processor.assign_and_connect(ubergraph, size)
if result is None:
return 'dispel4py.multi_process: ' \
'Not enough processes for execution of graph'
processes, inputmappings, outputmappings = result
inputs = processor.map_inputs_to_partitions(ubergraph, inputs)
success = True
nodes = [node.getContainedObject()
for node in ubergraph.graph.nodes()]
except:
print(traceback.format_exc())
return 'dispel4py.multi_process: ' \
'Could not create mapping for execution of graph'
print('Processes: %s' % processes)
# print ("inputmappings: %s, \noutputmappings: %s" % (inputmappings, outputmappings))
process_pes = {}
queues = {}
result_queue = None
try:
if args.results:
result_queue = multiprocessing.Queue()
except AttributeError:
pass
for pe in nodes:
provided_inputs = processor.get_inputs(pe, inputs)
for proc in processes[pe.id]:
cp = copy.deepcopy(pe)
cp.rank = proc
cp.log = types.MethodType(simpleLogger, cp)
if args.profileOn:
wrapper = MultiProcessingWrapper(proc, cp, provided_inputs, workflow_submission_id = workflow_submission_id, profiles = profiles)
else:
wrapper = MultiProcessingWrapper(proc, cp, provided_inputs, workflow_submission_id = workflow_submission_id)
process_pes[proc] = wrapper
wrapper.input_queue = multiprocessing.Queue()
wrapper.input_queue.name = 'Queue_%s_%s' % (cp.id, cp.rank)
wrapper.result_queue = result_queue
queues[proc] = wrapper.input_queue
wrapper.targets = outputmappings[proc]
wrapper.sources = inputmappings[proc]
for proc in process_pes:
wrapper = process_pes[proc]
wrapper.output_queues = {}
for target in wrapper.targets.values():
for inp, comm in target:
for i in comm.destinations:
wrapper.output_queues[i] = queues[i]
jobs = []
for wrapper in process_pes.values():
p = multiprocessing.Process(target=_processWorker, args=(wrapper, ))
jobs.append(p)
for j in jobs:
j.start()
for j in jobs:
j.join()
if result_queue:
result_queue.put(STATUS_TERMINATED)
if args.profileOn:
t2 = time.time()
t3 = t2 - t1
profiles["exec_%s" % workflow_submission_id] = t3
profiles["submitted_%s" % workflow_submission_id] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(t1))
# print("Total execution workflow time is %s recorded by proccess %s" % (t3, cp.rank))
multi_monitor.get_pe_process_map(processes, workflow_submission_id)
multi_monitor.analyse_and_record()
return result_queue
