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# Demonstrate how to work with Python GPU arrays using CUDA-aware MPI.
# A GPU array is allocated and manipulated through Numba, which is
# compliant with the __cuda_array_interface__ standard.
#
# Run this script using the following command:
# mpiexec -n 2 python use_cupy.py
from mpi4py import MPI
from numba import cuda
import numpy
@cuda.jit()
def add_const(arr, value):
x = cuda.grid(1)
if x < arr.size:
arr[x] += value
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
# Send-Recv
if rank == 0:
buf = cuda.device_array((20,), dtype='f')
buf[:] = range(20)
block = 32
grid = (buf.size + block - 1)//block
add_const[grid, block](buf, 100)
# always make sure the GPU buffer is ready before any MPI operation
cuda.default_stream().synchronize()
comm.Send(buf, dest=1, tag=77)
else:
buf = cuda.device_array((20,), dtype='f')
cuda.default_stream().synchronize()
comm.Recv(buf, source=0, tag=77)
buf = buf.copy_to_host()
assert numpy.allclose(buf, 100+numpy.arange(20, dtype='f'))
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