PyPar is a python library that provides efficient and scalable parallelism using the message passing interface (MPI) to handle big data and highly computational problems.
PyPar is used by a number of large projects, such as:
- ANUGA shallow water equation solver
- TCRM A statistical-parametric model for assessing wind hazard from tropical cyclones
- Wind multipliers: for produce wind terrain, shielding and topographic multipliers
A simple 'pass the parcel' example.
import pypar as pp
ncpus = pp.size()
rank = pp.rank()
node = pp.get_processor_name()
print 'I am rank %d of %d on node %s' % (rank, ncpus, node)
if rank == 0:
msg = 'P0'
pp.send(msg, destination=1)
msg = pp.receive(source=rank-1)
print 'Processor 0 received message "%s" from rank %d' % (msg, rank-1)
else:
source = rank-1
destination = (rank+1) % ncpus
msg = pp.receive(source)
msg = msg + 'P' + str(rank)
pp.send(msg, destination)
pp.finalize()