A sparse matrix‐vector multiplication method with low preprocessing cost

Name: A sparse matrix‐vector multiplication method with low preprocessing cost
Author: Aktemur, Tankut Barış

İsim	A sparse matrix‐vector multiplication method with low preprocessing cost
Yazar	Aktemur, Tankut Barış
Basım Tarihi:	2018-11-10
Basım Yeri	- Wiley
Konu	Compressed sparse row, Sparse matrix-vector multiplication, SpMV
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1532-0626
Kayıt Numarası	69b650a0-940b-4121-9c13-bd222af4aa47
Lokasyon	Computer Science
Tarih	2018-11-10
Örnek Metin	Sparse matrix-vector multiplication (SpMV) is a crucial operation used for solving many engineering and scientific problems. In general, there is no single SpMV method that gives high performance for all sparse matrices. Even though there exist sparse matrix storage formats and SpMV implementations that yield high efficiency for certain matrix structures, using these methods may entail high preprocessing or format conversion costs. In this work, we present a new SpMV implementation, named CSRLenGoto, that can be utilized by preprocessing the Compressed Sparse Row (CSR) format of a matrix. This preprocessing phase is inexpensive enough for the associated cost to be compensated in just a few repetitions of the SpMV operation. CSRLenGoto is based on complete loop unrolling and gives performance improvements in particular for matrices whose mean row length is low. We parallelized our method by integrating it into a state-of-the-art matrix partitioning approach as the kernel operation. We observed up to 2.46× and on the average 1.29× speedup with respect to Intel MKL's SpMV function for matrices with short- or medium-length rows.
DOI	10.1002/cpe.4701
Cilt	30

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A sparse matrix‐vector multiplication method with low preprocessing cost

Yazar Aktemur, Tankut Barış

Basım Tarihi 2018-11-10

Basım Yeri - Wiley

Konu Compressed sparse row, Sparse matrix-vector multiplication, SpMV

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1532-0626

Kayıt Numarası 69b650a0-940b-4121-9c13-bd222af4aa47

Lokasyon Computer Science

Tarih 2018-11-10

Örnek Metin Sparse matrix-vector multiplication (SpMV) is a crucial operation used for solving many engineering and scientific problems. In general, there is no single SpMV method that gives high performance for all sparse matrices. Even though there exist sparse matrix storage formats and SpMV implementations that yield high efficiency for certain matrix structures, using these methods may entail high preprocessing or format conversion costs. In this work, we present a new SpMV implementation, named CSRLenGoto, that can be utilized by preprocessing the Compressed Sparse Row (CSR) format of a matrix. This preprocessing phase is inexpensive enough for the associated cost to be compensated in just a few repetitions of the SpMV operation. CSRLenGoto is based on complete loop unrolling and gives performance improvements in particular for matrices whose mean row length is low. We parallelized our method by integrating it into a state-of-the-art matrix partitioning approach as the kernel operation. We observed up to 2.46× and on the average 1.29× speedup with respect to Intel MKL's SpMV function for matrices with short- or medium-length rows.

DOI 10.1002/cpe.4701

Cilt 30