High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations

High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations

Bibliographic Details
Main Author: Godwin, Jeswin Samuel
Language:English
Published: The Ohio State University / OhioLINK 2013
Subjects:
Computer Engineering
Computer Science
"SPMV
GPU
Structured Grid
Column-Diagonal"
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=osu1357280824
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-osu13572808242021-08-03T05:20:09Z High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations Godwin, Jeswin Samuel Computer Engineering Computer Science "SPMV GPU Structured Grid Column-Diagonal" In this thesis, we address efficient sparse matrix-vector multiplication for matrices arising from structured grid problems with high degrees of freedom at each grid node. Sparse matrix-vector multiplication is a critical step in the iterative solution of sparse linear systems of equations arising in the solution of partial differential equations using uniform grids for discretization. With uniform grids, the resulting linear system Ax = b has a matrix A that is sparse with a very regular structure. The specific focus of this thesis is on sparse matrices that have a block structure due to the large number of unknowns at each grid point. Sparse matrix storage formats such as Compressed Sparse Row (CSR) and Diagonal format (DIA) are not the most effective for such matrices.In this thesis, we present a new sparse matrix storage format that takes advantage of the diagonal structure of matrices for stencil operations on structured grids. Unlike other formats such as the Diagonal storage format (DIA), we specifically optimize for the case of higher degrees of freedom, where formats such as DIA are forced to explicitly represent many zero elements in the sparse matrix. We develop efficient sparse matrix-vector multiplication for structured grid computations on GPU architectures using CUDA. 2013-05-22 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1357280824 http://rave.ohiolink.edu/etdc/view?acc_num=osu1357280824 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Computer Engineering
Computer Science
"SPMV
GPU
Structured Grid
Column-Diagonal"
spellingShingle Computer Engineering
Computer Science
"SPMV
GPU
Structured Grid
Column-Diagonal"
Godwin, Jeswin Samuel
High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations
author Godwin, Jeswin Samuel
author_facet Godwin, Jeswin Samuel
author_sort Godwin, Jeswin Samuel
title High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations
title_short High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations
title_full High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations
title_fullStr High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations
title_full_unstemmed High-Performancs Sparse Matrix-Vector Multiplication on GPUS for Structured Grid Computations
title_sort high-performancs sparse matrix-vector multiplication on gpus for structured grid computations
publisher The Ohio State University / OhioLINK
publishDate 2013
url http://rave.ohiolink.edu/etdc/view?acc_num=osu1357280824
work_keys_str_mv AT godwinjeswinsamuel highperformancssparsematrixvectormultiplicationongpusforstructuredgridcomputations
_version_ 1719418739125911552

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