This package implements the DFM class.
The DFM is a useful abstraction for working with lists distributed over a set of MPI ranks. The acronym stands for distributed free monoid, which is just a fancy way to say it's a list.
If you're familiar with spark, it's like an RDD, but only holds a list.
from mpi_list import Context, DFM
C = Context() # calls MPI_Init via mpi4py
# After each of the three lines below:
# 1. each rank now has 1000//C.procs consecutive numbers
# 2. each rank now has a list of strings
# 3. only numbers containing a '2' remain
dfm = C . iterates(1000) \
. map(lambda i: f"String {i}") \
. filter(lambda s: '2' in s)
if C.rank == 0:
# Caution! Uncommenting this will deadlock your program.
# Collective calls must be called by all ranks!
#print( dfm . head(10) )
pass
# This is OK, since all ranks now have 'ans'
ans = dfm.head(10)
if C.rank == 0:
print( ans )
ans = dfm . filter(lambda s: len(s) <= len("String nn")) \
. collect()
if ans is not None: # only rank 0 gets "collect"
print( ans )Launch your program with mpirun python my_prog.py.
If you're using a supercomputer, consider installing spindle, and then use spindle mpirun python my_prog.py.
This project has been set up using PyScaffold 4.0.1. For details and usage information on PyScaffold see https://pyscaffold.org/.