Author
Yılmaz, Buse, Aktemur, Tankut Barış, Garzaran, M. J., Kamin, S., Kıraç, Mustafa Furkan
Publication Date
2016-04
Publication Place
-
ACM
Subject
Performance, Experimentation, Measurement, Autotuning, Runtime code generation, Sparse matrix-vector multiplication
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1544-3973
Record ID
35155ed4-cde3-432f-be0b-588d015a69fe
Library Location
Computer Science
Date
2016-04
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
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
