Parallel Summation using MPI in Python with mpi4py

Parallel summation involves distributing the task of summing a large set of numbers across multiple processors or computing nodes, enabling simultaneous computation and aggregation of partial results. Each processor handles a portion of the data, performs local summation, and then communicates its partial sum to a designated root processor. The root processor collects and combines these partial sums to compute the global sum, thereby leveraging parallelism to accelerate the computation process and efficiently handle large-scale data sets. In this tutorial, we explore the implementation of parallel summation using MPI in Python, demonstrating how MPI facilitates communication and coordination among processes to achieve efficient parallel computation. Visit the detailed tutorial on MPI in Python here.

Code Example

from mpi4py import MPI

comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
# Define the total number of elements
N = 100
# Calculate the number of elements to be handled by each process
chunk_size = N / size
start = rank * chunk_size
end = start + chunk_size
# Perform local summation for each process
local_sum = sum(range(start + 1, end + 1))
# Gather all local sums to the…