MPI with Python

MPI with Python

This practical lab leads you into the world of parallel processing with MPI (Message Passing Interface) and Python, mpi4py. We’ll bypass extensive theory for now and dive straight into coding exercises that showcase the power of distributed computing. For theory, you can visit the detailed course here.

Point to Point Communication in MPI

MPI (Message Passing Interface) is a standardized and widely used communication protocol for parallel computing. It allows processes running on ...

Running MPI4py on Jupyter Notebook – Step-by-Step Guide

Running MPI4py on Jupyter Notebook enables parallel computing within an interactive and user-friendly environment. This guide provides a step-by-step approach ...

Blocking and Non-blocking Communication in MPI

In parallel computing with MPI (Message Passing Interface), communication between processes plays a crucial role in achieving efficient parallelization of ...

MPI with Python: Calculating Squares of Array Elements Using Multiple Processors

In this lab tutorial, we will explore how to utilize multiple processors to compute the squares of elements in an ...

MPI Gather Function in Python

The gather function is used to gather data from multiple processes into a single process. We'll go through the provided ...

MPI: Concurrent File I/O for by Multiple Processes

In this tutorial, we'll explore an MPI (Message Passing Interface) program using mpi4py to demonstrate how multiple processors can collectively ...

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 ...

Matrix Multiplication on Multi-Processors: MPI4PY

In this scenario, each processor handles a portion of the matrices, performing computations independently, and then the results are combined ...
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