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Category: MPI with Python

MPI: Concurrent File I/O for by Multiple Processes

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 write to and read from a shared file. Code Code Explanation Imports the necessary MPI module from mpi4py which provides bindings for MPI functionality in Python. Initializes MPI communication (comm) for all processes (MPI.COMM_WORLD). rank is assigned the unique identifier (rank) of the current process, and size represents the total number of processes. Sets the name of the file (output.txt)…

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MPI Gather Function in Python

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 code, line by line, and understand how the gather function works. The detailed tutorial can be found here. Code Explanation This line imports the MPI functionality from the mpi4py library. These lines initialize the MPI communicator (comm) and obtain the total number of processes (size) and the rank of the current process (rank). Each process calculates its own data value…

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

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 array concurrently using the MPI (Message Passing Interface) library in Python, specifically using the mpi4py module. MPI is a widely-used standard for parallel computing in distributed memory systems. We’ll create a master-worker model where the master process distributes tasks to worker processes, each responsible for computing the square of a subset of the array elements. The detailed tutorial of MPI…

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Blocking and Non-blocking Communication in MPI

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 algorithms. Two common approaches to communication are blocking and non-blocking communication. You can visit the detailed tutorial on MPI with Python here. Blocking Communication Blocking communication involves processes halting their execution until the communication operation is complete. In MPI, blocking communication functions like comm.send() and comm.recv() ensure that the sender waits until the receiver receives the message, and vice versa….

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

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 to setting up and executing MPI (Message Passing Interface) Python programs using MPI4py library in a Jupyter Notebook. Whether you’re operating on Windows or Ubuntu, this tutorial caters to users on both platforms. From installing the necessary components to executing MPI programs with multiple processors, each step is meticulously outlined to facilitate seamless integration and efficient utilization of MPI4py within…

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Point to Point Communication in MPI

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 different nodes of a parallel system to communicate with each other. MPI is available in several programming languages, including C, C++, and Python, among others. In this tutorial, we’ll focus on using MPI in Python, specifically with the mpi4py library. Availability of MPI MPI is available in multiple programming languages, making it accessible to a wide range of developers. Here’s…

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