College of Saint Benedict and Saint John's University DigitalCommons@CSB/SJU All College Thesis Program, 2016-2019 Honors Program Spring 2019 Reducing Memory Access Latencies using Data Compression in Sparse, Iterative Linear Solvers Neil Lindquist
[email protected] Follow this and additional works at: https://digitalcommons.csbsju.edu/honors_thesis Part of the Numerical Analysis and Computation Commons, and the Numerical Analysis and Scientific Computing Commons Recommended Citation Lindquist, Neil, "Reducing Memory Access Latencies using Data Compression in Sparse, Iterative Linear Solvers" (2019). All College Thesis Program, 2016-2019. 63. https://digitalcommons.csbsju.edu/honors_thesis/63 Copyright 2019 Neil Lindquist Reducing Memory Access Latencies using Data Compression in Sparse, Iterative Linear Solvers An All-College Thesis College of Saint Benedict/Saint John's University by Neil Lindquist April 2019 Project Title: Reducing Memory Access Latencies using Data Compression in Sparse, Iterative Linear Solvers Approved by: Mike Heroux Scientist in Residence Robert Hesse Associate Professor of Mathematics Jeremy Iverson Assistant Professor of Computer Science Bret Benesh Chair, Department of Mathematics Imad Rahal Chair, Department of Computer Science Director, All College Thesis Program i Abstract Solving large, sparse systems of linear equations plays a significant role in cer- tain scientific computations, such as approximating the solutions of partial dif- ferential equations. However, solvers for these types of problems usually spend most of their time fetching data from main memory. In an effort to improve the performance of these solvers, this work explores using data compression to reduce the amount of data that needs to be fetched from main memory. Some compression methods were found that improve the performance of the solver and problem found in the HPCG benchmark, with an increase in floating point operations per second of up to 84%.