Parallel_Programming

Parallel param programming is often used interchangeably with parallel processing or in connection with parallel computing, which refers to systems that enhance the efficiency of parallel execution. In parallel programming, tasks are divided and executed simultaneously across multiple computers or multiple CPU cores. This approach is essential for large-scale projects where speed and precision are crucial. Although complex, parallel programming enables developers, researchers, and users to perform research and analysis much faster than programs limited to processing tasks sequentially

Project Description

This project demonstrates the use of parallel programming techniques in C++ with (OpenMP) and (MPI) to efficiently execute computations across multiple threads. It includes examples of parallel loops, task scheduling, and thread synchronization. The goal is to showcase performance improvements in computational tasks using OpenMP and MPI directives.

1. OpenMP (Open Multi-Processing)

OpenMP is a multi-platform programming interface that enables multiprocessing programming. OpenMP can be used in C++, C and Fortran languages, including different architectures like Windows and Unix. It consists of compilator directives that have an impact on code execution.

The OpenMP interface is a component of the GNU Compiler Collection (GCC), a set of open-source compilers developed by the GNU Project. GCC compiler is therefore highly recommended for use with OpenMP, although it is not required (there is an Intel compiler that also support OpenMP).

INSTALLATION AND CONFIGURATION ON LINUX SYSTEMS:

Start the terminal and update the repository:

>>> sudo apt-get update

Then install the build-essential package, including gcc, g++ and make:

>>> sudo apt-get install build-essential

2. MPI

Message Passing Interface (MPI) is a communication protocol standard for transferring messages between parallel program processes on one or more computers. MPI is currently the most widely used communication model in clusters of computers and supercomputers.

There are several implementations of MPI, including OpenMPI, MPICH and MSMPI. On Linux, we can choose from OpenMPI and MPICH, while MSMPI is a Windows implementation. Before going any further, we should ensure that we have the GCC compiler installed.

INSTALLATION AND CONFIGURATION OF MPICH ON LINUX SYSTEMS:

Start the terminal and update the repository:

>>> sudo apt-get update

We then install the mpich package:

>>> sudo apt-get install mpich

We can now check the version of the installed MPI (this will actually be the GCC version):

>>> mpic++ --version

Here you can find out more about MPICH: https://www.mpich.org/.

THE INSTALLATION PROCESS UNDER WINDOWS IS COMPLEX, AND I DO NOT RECOMMEND USING MPI WITH THE WINDOWS PLATFORM

NOTE

Linux is often recommended for compiling parallel programs For optimal performance and seamless development, we recommend using Linux for building and running this project.

Project Tree

This project contains two branches: main and Advanced.
The main branch includes operations from the NumPy module that have been translated into C++ using OpenMP and MPI for parallel processing.

main

.
|-- include
|   |-- array_concat.h
|   |-- dot_product.h
|   |-- find_max.h
|   |-- lu_decomposition.h
|   |-- matrix_mult.h
|   |-- matrix_vector.h
|   |-- montecarlo.h
|   |-- parallel_sum.h
|   |-- pi_calculator.h
|   |-- product_log.h
|   |-- statistics.h
|   |-- sum2.h
|   |-- sum_task.h
|
|-- src
|   |-- Histogram/
|   |   |-- histo.cpp
|   |   |-- data.txt
|   |-- Contributing.md
|   |-- LU_factorisation.cpp
|   |-- README.md
|   |-- algoimage.png
|   |-- broadcast.cpp
|   |-- concatenate.cpp
|   |-- input.txt(for max.cpp)
|   |-- max.cpp
|   |-- mm.cpp  (Matrix Multiplication implementation)
|   |-- montecarlo.cpp
|   |-- mv.cpp (Matrix Vector multiplication)
|   |-- pi-reduction.cpp (Parallel pi computation using OpenMP reduction)
|   |-- prod.cpp (Parallel product computation)
|   |-- standard_dev.cpp
|   |-- sum2.cpp
|   |-- sum_task.cpp
|   |-- vvd.cpp (Vector-Vector Dot product)
|   |-- wrong_sum.cpp (Demo of a wrong summation example for learning purposes)

Advanced

.
|-- include
|   |-- array_concat.h
|   |-- dot_product.h
|   |-- find_max.h
|   |-- lu_decomposition.h
|   |-- matrix_mult.h
|   |-- matrix_vector.h
|   |-- montecarlo.h
|   |-- parallel_sum.h
|   |-- pi_calculator.h
|   |-- product_log.h
|   |-- statistics.h
|   |-- sum2.h
|   |-- sum_task.h
|
|-- src
|   |-- Histogram/
|   |   |-- histo.cpp
|   |   |-- data.txt
|   |-- Contributing.md
|   |-- LU_factorisation.cpp
|   |-- README.md
|   |-- algoimage.png
|   |-- broadcast.cpp
|   |-- concatenate.cpp
|   |-- input.txt(for max.cpp)
|   |-- max.cpp
|   |-- mm.cpp  (Matrix Multiplication implementation)
|   |-- montecarlo.cpp
|   |-- mv.cpp (Matrix Vector multiplication)
|   |-- pi-reduction.cpp (Parallel pi computation using OpenMP reduction)
|   |-- prod.cpp (Parallel product computation)
|   |-- standard_dev.cpp
|   |-- sum2.cpp
|   |-- sum_task.cpp
|   |-- vvd.cpp (Vector-Vector Dot product)
|   |-- wrong_sum.cpp (Demo of a wrong summation example for learning purposes)
|-- Application
|   |-- page-rank.cpp

Note

Information about Functions in main is provided in README.md
For contributing to this repo kindly go through the guidelines provided in Contributing.md