Preconditioner auto-tuning using deep learning for sparse iterative algorithms

Kenya Yamada, Takahiro Katagiri, Hiroyuki Takizawa, Kazuo Minami, Mitsuo Yokokawa, Toru Nagai, Masao Ogino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In numerical libraries for sparse matrix operations, there are many tuning parameters related to implementation selection. Selection of different tuning parameters could result in totally different performance. Moreover, optimal implementation depends on the sparse matrices to be operated. It is difficult to find optimal implementation without executing each implementation and thereby examining its performance on a given sparse matrix. In this study, we propose an implementation selection method for sparse iterative algorithms and preconditioners in a numerical library using deep learning. The proposed method uses full color images to represent the features of a sparse matrix. We present an image generation method for partitioning a given matrix (to generate its feature image) so that the value of each matrix element is considered in the implementation selection. We then evaluate the effectiveness of the proposed method by conducting a numerical experiment. In this experiment, the accuracy of implementation selection is evaluated. The training data comprise a pair of sparse matrix and its optimal implementation. The optimal implementation of each sparse matrix in the training data is obtained in advance by executing every implementation and getting the best one. The experimental results obtained using the proposed method show that the accuracy of selecting the optimal implementation of each sparse matrix is 79.5%.

Original languageEnglish
Title of host publicationProceedings - 2018 6th International Symposium on Computing and Networking Workshops, CANDARW 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages257-262
Number of pages6
ISBN (Electronic)9781538691847
DOIs
Publication statusPublished - 2018 Dec 26
Event6th International Symposium on Computing and Networking Workshops, CANDARW 2018 - Takayama, Japan
Duration: 2018 Nov 272018 Nov 30

Publication series

NameProceedings - 2018 6th International Symposium on Computing and Networking Workshops, CANDARW 2018

Conference

Conference6th International Symposium on Computing and Networking Workshops, CANDARW 2018
CountryJapan
CityTakayama
Period18/11/2718/11/30

Keywords

  • Auto-tuning
  • Deep learning
  • GMRES
  • Preconditioner selection
  • Xabclib

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Statistics, Probability and Uncertainty
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Preconditioner auto-tuning using deep learning for sparse iterative algorithms'. Together they form a unique fingerprint.

Cite this