Analysis of the Performance of the Matrix Multiplication Algorithm on the Cirrus Supercomputer

TL;DR

This paper evaluates the performance and scalability of serial and parallel matrix multiplication algorithms on the Cirrus supercomputer, offering insights for optimizing large-scale computations in scientific and engineering applications.

Contribution

It provides a comprehensive analysis of matrix multiplication performance on Cirrus, highlighting optimization strategies for large-scale parallel processing.

Findings

Parallel algorithms scale efficiently on Cirrus

Serial and parallel performance benchmarks established

Insights for optimizing matrix multiplication in real-world scenarios

Abstract

Matrix multiplication is integral to various scientific and engineering disciplines, including machine learning, image processing, and gaming. With the increasing data volumes in areas like machine learning, the demand for efficient parallel processing of large matrices has grown significantly.This study explores the performance of both serial and parallel matrix multiplication on the Cirrus supercomputer at the University of Edinburgh. The results demonstrate the scalability and efficiency of these methods, providing insights for optimizing matrixmultiplication in real-world applications.