def RunMasterSlave(masterClass, slaveClass, workParams, useMPI=True, finalRun=True):
"""
This is a generic master/slave runner.
"""
if HAVE_MPI and useMPI:
MSRunner = SimpleMasterSlave(masterClass, slaveClass, workParams)
balancer = PyparBalancer(MSRunner, False)
balancer.run(finalRun)
if MY_RANK==0:
return MSRunner.getMasterInstance()
else:
#
# run serially in case there's no MPI or the called doesn't want it.
#
master = masterClass()
slave = slaveClass()
for i in range(len(workParams)):
master(i,slave(workParams[i]))
return master
def RunMasterSlave(masterClass, slaveClass, workParams, useMPI=True, finalRun=True):
"""
This is a generic master/slave runner.
"""
if HAVE_MPI and useMPI:
MSRunner = SimpleMasterSlave(masterClass, slaveClass, workParams)
balancer = PyparBalancer(MSRunner, False)
balancer.run(finalRun)
if MY_RANK==0:
return MSRunner.getMasterInstance()
else:
#
# run serially in case there's no MPI or the called doesn't want it.
#
master = masterClass()
slave = slaveClass()
for i in range(len(workParams)):
master(i,slave(workParams[i]))
return master
def foreach(f, l, useMPI=True, return_=True, debug=False, finalRun=True):
"""
for each element in list 'l' apply the function 'f'.
You can force serial operation by setting useMPI to 'False'
The last time you're call foreach... make sure finalRun=True.
"""
if HAVE_MPI and useMPI:
if debug and MY_RANK == 0:
print "Found MPI environment with multiple nodes.. using MPI!"
GMPI = GenericMPI(f, l, debug=debug)
balancer = PyparBalancer(
GMPI, False) # set this to True to get lots of debugging output
balancer.run(finalRun)
if return_:
if MY_RANK == 0:
return GMPI.results
else:
return "You don't get results in this rank!:" + ` MY_RANK `
else:
#
# just run serially
#
if debug:
print "No MPI environment with multiple nodes.. evaluating serially."
if return_:
results = []
for v in l:
result = f(v)
if debug:
print "Work item completed:", len(results), ':', result
results.append(result)
return results
else:
for v in l:
f(v)
return
def foreach(f, l, useMPI=True, return_=True, debug=False, finalRun=True):
"""
for each element in list 'l' apply the function 'f'.
You can force serial operation by setting useMPI to 'False'
The last time you're call foreach... make sure finalRun=True.
"""
if HAVE_MPI and useMPI:
if debug and MY_RANK==0:
print "Found MPI environment with multiple nodes.. using MPI!"
GMPI = GenericMPI(f, l, debug=debug)
balancer = PyparBalancer(GMPI, False) # set this to True to get lots of debugging output
balancer.run(finalRun)
if return_:
if MY_RANK==0:
return GMPI.results
else:
return "You don't get results in this rank!:" + `MY_RANK`
else:
#
# just run serially
#
if debug:
print "No MPI environment with multiple nodes.. evaluating serially."
if return_:
results=[]
for v in l:
result = f(v)
if debug:
print "Work item completed:", len(results), ':', result
results.append(result)
return results
else:
for v in l:
f(v)
return
