Autotuning runtime specialization for sparse matrix-vector multiplication

Name: Autotuning runtime specialization for sparse matrix-vector multiplication
Author: Yılmaz, Buse, Aktemur, Tankut Barış, Garzaran, M. J., Kamin, S., Kıraç, Mustafa Furkan

İsim	Autotuning runtime specialization for sparse matrix-vector multiplication
Yazar	Yılmaz, Buse, Aktemur, Tankut Barış, Garzaran, M. J., Kamin, S., Kıraç, Mustafa Furkan
Basım Tarihi:	2016-04
Basım Yeri	- ACM
Konu	Performance, Experimentation, Measurement, Autotuning, Runtime code generation, Sparse matrix-vector multiplication
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1544-3973
Kayıt Numarası	35155ed4-cde3-432f-be0b-588d015a69fe
Lokasyon	Computer Science
Tarih	2016-04
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	Runtime specialization is used for optimizing programs based on partial information available only at runtime. In this paper we apply autotuning on runtime specialization of Sparse Matrix-Vector Multiplication to predict a best specialization method among several. In 91% to 96% of the predictions, either the best or the second-best method is chosen. Predictions achieve average speedups that are very close to the speedups achievable when only the best methods are used. By using an efficient code generator and a carefully designed set of matrix features, we show the runtime costs can be amortized to bring performance benefits for many real-world cases.
DOI	10.1145/2851500
Cilt	13

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Autotuning runtime specialization for sparse matrix-vector multiplication

Yazar Yılmaz, Buse, Aktemur, Tankut Barış, Garzaran, M. J., Kamin, S., Kıraç, Mustafa Furkan

Basım Tarihi 2016-04

Basım Yeri - ACM

Konu Performance, Experimentation, Measurement, Autotuning, Runtime code generation, Sparse matrix-vector multiplication

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1544-3973

Kayıt Numarası 35155ed4-cde3-432f-be0b-588d015a69fe

Lokasyon Computer Science

Tarih 2016-04

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

Örnek Metin Runtime specialization is used for optimizing programs based on partial information available only at runtime. In this paper we apply autotuning on runtime specialization of Sparse Matrix-Vector Multiplication to predict a best specialization method among several. In 91% to 96% of the predictions, either the best or the second-best method is chosen. Predictions achieve average speedups that are very close to the speedups achievable when only the best methods are used. By using an efficient code generator and a carefully designed set of matrix features, we show the runtime costs can be amortized to bring performance benefits for many real-world cases.

DOI 10.1145/2851500

Cilt 13