Optimization by runtime specialization for sparse matrix-vector multiplication

Name: Optimization by runtime specialization for sparse matrix-vector multiplication
Author: Kamin, S., Jesus Garzaran, M., Aktemur, Tankut Barış, Xu, D., Yılmaz, Buse, Chen, Z.

İsim	Optimization by runtime specialization for sparse matrix-vector multiplication
Yazar	Kamin, S., Jesus Garzaran, M., Aktemur, Tankut Barış, Xu, D., Yılmaz, Buse, Chen, Z.
Basım Tarihi:	2014
Basım Yeri	- ACM
Konu	Program specialization, Sparce matrix-vector multiplication, Performance evaluation
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-4503-3161-6
Kayıt Numarası	aa48d919-acd4-42d5-8cdd-f444b9366c51
Lokasyon	Computer Science
Tarih	2014
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Runtime specialization optimizes programs based on partial information available only at run time. It is applicable when some input data is used repeatedly while other input data varies. This technique has the potential of generating highly efficient codes. In this paper, we explore the potential for obtaining speedups for sparse matrix-dense vector multiplication using runtime specialization, in the case where a single matrix is to be multiplied by many vectors. We experiment with five methods involving runtime specialization, comparing them to methods that do not (including Intel's MKL library). For this work, our focus is the evaluation of the speedups that can be obtained with runtime specialization without considering the overheads of the code generation. Our experiments use 23 matrices from the Matrix Market and Florida collections, and run on five different machines. In 94 of those 115 cases, the specialized code runs faster than any version without specialization. If we only use specialization, the average speedup with respect to Intel's MKL library ranges from 1.44x to 1.77x, depending on the machine. We have also found that the best method depends on the matrix and machine; no method is best for all matrices and machines.
DOI	10.1145/2658761.2658773

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Optimization by runtime specialization for sparse matrix-vector multiplication

Yazar Kamin, S., Jesus Garzaran, M., Aktemur, Tankut Barış, Xu, D., Yılmaz, Buse, Chen, Z.

Basım Tarihi 2014

Basım Yeri - ACM

Konu Program specialization, Sparce matrix-vector multiplication, Performance evaluation

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-4503-3161-6

Kayıt Numarası aa48d919-acd4-42d5-8cdd-f444b9366c51

Lokasyon Computer Science

Tarih 2014

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin Runtime specialization optimizes programs based on partial information available only at run time. It is applicable when some input data is used repeatedly while other input data varies. This technique has the potential of generating highly efficient codes. In this paper, we explore the potential for obtaining speedups for sparse matrix-dense vector multiplication using runtime specialization, in the case where a single matrix is to be multiplied by many vectors. We experiment with five methods involving runtime specialization, comparing them to methods that do not (including Intel's MKL library). For this work, our focus is the evaluation of the speedups that can be obtained with runtime specialization without considering the overheads of the code generation. Our experiments use 23 matrices from the Matrix Market and Florida collections, and run on five different machines. In 94 of those 115 cases, the specialized code runs faster than any version without specialization. If we only use specialization, the average speedup with respect to Intel's MKL library ranges from 1.44x to 1.77x, depending on the machine. We have also found that the best method depends on the matrix and machine; no method is best for all matrices and machines.

DOI 10.1145/2658761.2658773