IEICE TRANSACTIONS on Information
A Machine Learning-Based Approach for Selecting SpMV Kernels and Matrix Storage Formats
Summary :
Sparse Matrix-Vector multiplication (SpMV) is a computational kernel widely used in many applications. Because of the importance, many different implementations have been proposed to accelerate this computational kernel. The performance characteristics of those SpMV implementations are quite different, and it is basically difficult to select the implementation that has the best performance for a given sparse matrix without performance profiling. One existing approach to the SpMV best-code selection problem is by using manually-predefined features and a machine learning model for the selection. However, it is generally hard to manually define features that can perfectly express the characteristics of the original sparse matrix necessary for the code selection. Besides, some information loss would happen by using this approach. This paper hence presents an effective deep learning mechanism for SpMV code selection best suited for a given sparse matrix. Instead of using manually-predefined features of a sparse matrix, a feature image and a deep learning network are used to map each sparse matrix to the implementation, which is expected to have the best performance, in advance of the execution. The benefits of using the proposed mechanism are discussed by calculating the prediction accuracy and the performance. According to the evaluation, the proposed mechanism can select an optimal or suboptimal implementation for an unseen sparse matrix in the test data set in most cases. These results demonstrate that, by using deep learning, a whole sparse matrix can be used to do the best implementation prediction, and the prediction accuracy achieved by the proposed mechanism is higher than that of using predefined features.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.9 pp.2307-2314
- Publication Date
- 2018/09/01
- Publicized
- 2018/06/13
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7176
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Hang CUI
Tohoku University
Shoichi HIRASAWA
National Institute of Informatics
Hiroaki KOBAYASHI
Tohoku University
Hiroyuki TAKIZAWA
Tohoku University
Keyword
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Hang CUI, Shoichi HIRASAWA, Hiroaki KOBAYASHI, Hiroyuki TAKIZAWA, "A Machine Learning-Based Approach for Selecting SpMV Kernels and Matrix Storage Formats" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 9, pp. 2307-2314, September 2018, doi: 10.1587/transinf.2017EDP7176.
Abstract: Sparse Matrix-Vector multiplication (SpMV) is a computational kernel widely used in many applications. Because of the importance, many different implementations have been proposed to accelerate this computational kernel. The performance characteristics of those SpMV implementations are quite different, and it is basically difficult to select the implementation that has the best performance for a given sparse matrix without performance profiling. One existing approach to the SpMV best-code selection problem is by using manually-predefined features and a machine learning model for the selection. However, it is generally hard to manually define features that can perfectly express the characteristics of the original sparse matrix necessary for the code selection. Besides, some information loss would happen by using this approach. This paper hence presents an effective deep learning mechanism for SpMV code selection best suited for a given sparse matrix. Instead of using manually-predefined features of a sparse matrix, a feature image and a deep learning network are used to map each sparse matrix to the implementation, which is expected to have the best performance, in advance of the execution. The benefits of using the proposed mechanism are discussed by calculating the prediction accuracy and the performance. According to the evaluation, the proposed mechanism can select an optimal or suboptimal implementation for an unseen sparse matrix in the test data set in most cases. These results demonstrate that, by using deep learning, a whole sparse matrix can be used to do the best implementation prediction, and the prediction accuracy achieved by the proposed mechanism is higher than that of using predefined features.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7176/_p
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@ARTICLE{e101-d_9_2307,
author={Hang CUI, Shoichi HIRASAWA, Hiroaki KOBAYASHI, Hiroyuki TAKIZAWA, },
journal={IEICE TRANSACTIONS on Information},
title={A Machine Learning-Based Approach for Selecting SpMV Kernels and Matrix Storage Formats},
year={2018},
volume={E101-D},
number={9},
pages={2307-2314},
abstract={Sparse Matrix-Vector multiplication (SpMV) is a computational kernel widely used in many applications. Because of the importance, many different implementations have been proposed to accelerate this computational kernel. The performance characteristics of those SpMV implementations are quite different, and it is basically difficult to select the implementation that has the best performance for a given sparse matrix without performance profiling. One existing approach to the SpMV best-code selection problem is by using manually-predefined features and a machine learning model for the selection. However, it is generally hard to manually define features that can perfectly express the characteristics of the original sparse matrix necessary for the code selection. Besides, some information loss would happen by using this approach. This paper hence presents an effective deep learning mechanism for SpMV code selection best suited for a given sparse matrix. Instead of using manually-predefined features of a sparse matrix, a feature image and a deep learning network are used to map each sparse matrix to the implementation, which is expected to have the best performance, in advance of the execution. The benefits of using the proposed mechanism are discussed by calculating the prediction accuracy and the performance. According to the evaluation, the proposed mechanism can select an optimal or suboptimal implementation for an unseen sparse matrix in the test data set in most cases. These results demonstrate that, by using deep learning, a whole sparse matrix can be used to do the best implementation prediction, and the prediction accuracy achieved by the proposed mechanism is higher than that of using predefined features.},
keywords={},
doi={10.1587/transinf.2017EDP7176},
ISSN={1745-1361},
month={September},}
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TY - JOUR
TI - A Machine Learning-Based Approach for Selecting SpMV Kernels and Matrix Storage Formats
T2 - IEICE TRANSACTIONS on Information
SP - 2307
EP - 2314
AU - Hang CUI
AU - Shoichi HIRASAWA
AU - Hiroaki KOBAYASHI
AU - Hiroyuki TAKIZAWA
PY - 2018
DO - 10.1587/transinf.2017EDP7176
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2018
AB - Sparse Matrix-Vector multiplication (SpMV) is a computational kernel widely used in many applications. Because of the importance, many different implementations have been proposed to accelerate this computational kernel. The performance characteristics of those SpMV implementations are quite different, and it is basically difficult to select the implementation that has the best performance for a given sparse matrix without performance profiling. One existing approach to the SpMV best-code selection problem is by using manually-predefined features and a machine learning model for the selection. However, it is generally hard to manually define features that can perfectly express the characteristics of the original sparse matrix necessary for the code selection. Besides, some information loss would happen by using this approach. This paper hence presents an effective deep learning mechanism for SpMV code selection best suited for a given sparse matrix. Instead of using manually-predefined features of a sparse matrix, a feature image and a deep learning network are used to map each sparse matrix to the implementation, which is expected to have the best performance, in advance of the execution. The benefits of using the proposed mechanism are discussed by calculating the prediction accuracy and the performance. According to the evaluation, the proposed mechanism can select an optimal or suboptimal implementation for an unseen sparse matrix in the test data set in most cases. These results demonstrate that, by using deep learning, a whole sparse matrix can be used to do the best implementation prediction, and the prediction accuracy achieved by the proposed mechanism is higher than that of using predefined features.
ER -
English
