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Parallel Programming Languages and Tools: MPI, OpenMPI, OpenMP, CUDA, TBB

Parallel Programming Languages and Tools: MPI, OpenMPI, OpenMP, CUDA, TBB

In the age of ever-growing devices, massive data and complex computations, the power of multiple processors simultaneously has become crucial. Parallel programming languages and frameworks provide the tools to break down problems into smaller tasks and execute them concurrently, significantly boosting performance. This guide introduces some of the most popular options: MPI, OpenMPI, CUDA, TBB, and Apache Spark. We’ll explore their unique strengths, delve into learning resources, and equip you to tackle the exciting world of parallel programming. Message Passing…

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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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Introduction to Parallel Programming Languages: Unlocking the Power of Multiple Processors

Introduction to Parallel Programming Languages: Unlocking the Power of Multiple Processors

As data sizes and computational demands grow, traditional sequential programming approaches often reach their limits. Parallel programming languages offer a solution by enabling us to harness the power of multiple processors simultaneously, significantly accelerating computations. This tutorial looks into the fundamentals of parallel programming languages, equipping you for the exciting world of parallel and distributed computing. You can visit the detailed tutorial here. Sequential vs. Parallel Programming: Understanding the Divide Sequential Programming: The traditional approach where instructions are executed one…

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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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