Correcting soft errors online in fast fourier transform

Xin Liang; Jieyang Chen; Dingwen Tao; Sihuan Li; Panruo Wu; Hongbo Li; Kaiming Ouyang; Yuanlai Liu; Fengguang Song; Zizhong Chen

doi:10.1145/3126908.3126915

Correcting soft errors online in fast fourier transform

Xin Liang, Jieyang Chen, Dingwen Tao, Sihuan Li, Panruo Wu, Hongbo Li, Kaiming Ouyang, Yuanlai Liu, Fengguang Song, Zizhong Chen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage.

Original language	English
Title of host publication	Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017
ISBN (Electronic)	9781450351140
DOIs	https://doi.org/10.1145/3126908.3126915
State	Published - Nov 12 2017
Event	International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017 - Denver, United States Duration: Nov 12 2017 → Nov 17 2017

Publication series

Name	Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017

Conference

Conference	International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017
Country/Territory	United States
City	Denver
Period	11/12/17 → 11/17/17

Bibliographical note

Publisher Copyright:
© 2017 ACM.

Funding

Œis work is partially supported by the NSF grants OAC-1305624, CCF-1513201, the SZSTI basic research program JCYJ2015063011494-2313, and the MOST key project 2017YFB0202100. This work is partially supported by the NSF grants OAC-1305624, CCF-1513201, the SZSTI basic research program JCYJ2015063011494-2313, and the MOST key project 2017YFB0202100.

Funders	Funder number
SZSTI	JCYJ2015063011494-2313
National Science Foundation Arctic Social Science Program	OAC-1305624, CCF-1513201
Ministry of Science and Technology	2017YFB0202100
National Science Foundation Arctic Social Science Program

Keywords

Algorithm-Based fault tolerance
DFT
FFT
FFTW
Soft errors

ASJC Scopus subject areas

Software
Computer Networks and Communications

Access to Document

10.1145/3126908.3126915

Cite this

Liang, X., Chen, J., Tao, D., Li, S., Wu, P., Li, H., Ouyang, K., Liu, Y., Song, F., & Chen, Z. (2017). Correcting soft errors online in fast fourier transform. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017 Article 30 (Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017). https://doi.org/10.1145/3126908.3126915

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title = "Correcting soft errors online in fast fourier transform",

abstract = "While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage.",

keywords = "Algorithm-Based fault tolerance, DFT, FFT, FFTW, Soft errors",

author = "Xin Liang and Jieyang Chen and Dingwen Tao and Sihuan Li and Panruo Wu and Hongbo Li and Kaiming Ouyang and Yuanlai Liu and Fengguang Song and Zizhong Chen",

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Liang, X, Chen, J, Tao, D, Li, S, Wu, P, Li, H, Ouyang, K, Liu, Y, Song, F & Chen, Z 2017, Correcting soft errors online in fast fourier transform. in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017., 30, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017, International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017, Denver, United States, 11/12/17. https://doi.org/10.1145/3126908.3126915

Correcting soft errors online in fast fourier transform. / Liang, Xin; Chen, Jieyang; Tao, Dingwen et al.
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017. 2017. 30 (Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Liang, Xin

AU - Chen, Jieyang

AU - Tao, Dingwen

AU - Li, Sihuan

AU - Wu, Panruo

AU - Li, Hongbo

AU - Ouyang, Kaiming

AU - Liu, Yuanlai

AU - Song, Fengguang

AU - Chen, Zizhong

PY - 2017/11/12

Y1 - 2017/11/12

N2 - While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage.

AB - While many algorithm-based fault tolerance (ABFT) schemes have been proposed to detect soft errors offline in the fast Fourier transform (FFT) after computation finishes, none of the existing ABFT schemes detect soft errors online before the computation finishes. This paper presents an online ABFT scheme for FFT so that soft errors can be detected online and the corrupted computation can be terminated in a much more timely manner. We also extend our scheme to tolerate both arithmetic errors and memory errors, develop strategies to reduce its fault tolerance overhead and improve its numerical stability and fault coverage, and finally incorporate it into the widely used FFTW library - one of the today's fastest FFT software implementations. Experimental results demonstrate that: (1) the proposed online ABFT scheme introduces much lower overhead than the existing offline ABFT schemes; (2) it detects errors in a much more timely manner; and (3) it also has higher numerical stability and better fault coverage.

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ER -

Liang X, Chen J, Tao D, Li S, Wu P, Li H et al. Correcting soft errors online in fast fourier transform. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017. 2017. 30. (Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2017). doi: 10.1145/3126908.3126915

Correcting soft errors online in fast fourier transform

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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