Along What Axis Does Mpi4py Scatterv Function Split A Numpy Array?

I have the following MWE using comm.Scatterv and comm.Gatherv to distribute a 4D array across a given number of cores (size) import numpy as np from mpi4py import MPI import matplo

Solution 1:

comm.Scatterv and comm.Gatherv do not know anything about the numpy array dimensions. They just see the sendbuf as a block of memory. Therefore it is necessary to take this into account when specifying the sendcounts and displacements (see http://materials.jeremybejarano.com/MPIwithPython/collectiveCom.html for details). The assumption is also that the data is laid out in C-style (row major) in memory.

An example for a 2D matrix is given below. The key parts of this code are to set split_sizes_input/split_sizes_output and displacements_input/displacements_output correctly. The code takes the second dimension size into account to specify the correct divisions in the memory block:

split_sizes_input = split_sizes*512

For higher dimensions, this line would be changed to:

split_sizes_input = split_sizes*indirect_dimension_sizes

where

indirect_dimension_sizes = npts2*npts3*npts4*....*nptsN

and likewise for split_sizes_output.

The code creates a 2D array with the numbers 1 to 512 incrementing across one dimension. It is easy to see from the plots if the data has been split and recombined correctly.

import numpy as np
from mpi4py import MPI
import matplotlib.pyplot as plt

comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()

if rank == 0:
    test = np.arange(0,512,dtype='float64')
    test = np.tile(test,[256,1]) #Create 2D input array. Numbers 1 to 512 increment across dimension 2.
    outputData = np.zeros([256,512]) #Create output array of same size
    split = np.array_split(test,size,axis = 0) #Split input array by the number of available cores

    split_sizes = []

    for i inrange(0,len(split),1):
        split_sizes = np.append(split_sizes, len(split[i]))

    split_sizes_input = split_sizes*512
    displacements_input = np.insert(np.cumsum(split_sizes_input),0,0)[0:-1]

    split_sizes_output = split_sizes*512
    displacements_output = np.insert(np.cumsum(split_sizes_output),0,0)[0:-1]


    print("Input data split into vectors of sizes %s" %split_sizes_input)
    print("Input data split with displacements of %s" %displacements_input)

    plt.imshow(test)
    plt.colorbar()
    plt.title('Input data')
    plt.show()

else:
#Create variables on other cores
    split_sizes_input = None
    displacements_input = None
    split_sizes_output = None
    displacements_output = None
    split = None
    test = None
    outputData = None

split = comm.bcast(split, root=0) #Broadcast split array to other cores
split_sizes = comm.bcast(split_sizes_input, root = 0)
displacements = comm.bcast(displacements_input, root = 0)
split_sizes_output = comm.bcast(split_sizes_output, root = 0)
displacements_output = comm.bcast(displacements_output, root = 0)

output_chunk = np.zeros(np.shape(split[rank])) #Create array to receive subset of data on each core, where rank specifies the coreprint("Rank %d with output_chunk shape %s" %(rank,output_chunk.shape))
comm.Scatterv([test,split_sizes_input, displacements_input,MPI.DOUBLE],output_chunk,root=0)

output = np.zeros([len(output_chunk),512]) #Create output array on each corefor i inrange(0,np.shape(output_chunk)[0],1):
    output[i,0:512] = output_chunk[i]

plt.imshow(output)
plt.title("Output shape %s for rank %d" %(output.shape,rank))
plt.colorbar()
plt.show()

print("Output shape %s for rank %d" %(output.shape,rank))

comm.Barrier()

comm.Gatherv(output,[outputData,split_sizes_output,displacements_output,MPI.DOUBLE], root=0) #Gather output data togetherif rank == 0:
    outputData = outputData[0:len(test),:]
    print("Final data shape %s" %(outputData.shape,))
    plt.imshow(outputData)
    plt.colorbar()
    plt.show()
    print(outputData)