Scalable Data Generation for Evaluating Mixed-Precision Solvers

Piotr Luszczek; Yaohung Tsai; Neil Lindquist; Hartwig Anzt; Jack Dongarra

doi:10.1109/HPEC43674.2020.9286145

Scalable Data Generation for Evaluating Mixed-Precision Solvers

Piotr Luszczek, Yaohung Tsai, Neil Lindquist, Hartwig Anzt, Jack Dongarra

Computer Science and Mathematics Div

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

Abstract

We present techniques of generating data for mixed precision solvers that allows to test those solvers in a scalable manner. Our techniques focus on mixed precision hardware and software where both the solver and the hardware can take advantage of mixing multiple floating precision formats. This allows taking advantage of recently released generation of hardware platforms that focus on ML and DNN workloads but can also be utilized for HPC applications if a new breed of algorithms is combined with the custom floating-point formats to deliver performance levels beyond the standard IEEE data types while delivering a comparable accuracy of the results.

Original language	English
Title of host publication	2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728192192
DOIs	https://doi.org/10.1109/HPEC43674.2020.9286145
State	Published - Sep 22 2020
Event	2020 IEEE High Performance Extreme Computing Conference, HPEC 2020 - Virtual, Waltham, United States Duration: Sep 21 2020 → Sep 25 2020

Publication series

Name	2020 IEEE High Performance Extreme Computing Conference, HPEC 2020

Conference

Conference	2020 IEEE High Performance Extreme Computing Conference, HPEC 2020
Country/Territory	United States
City	Virtual, Waltham
Period	09/21/20 → 09/25/20

Funding

This research was supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.

Access to Document

10.1109/HPEC43674.2020.9286145

Cite this

Luszczek, P., Tsai, Y., Lindquist, N., Anzt, H., & Dongarra, J. (2020). Scalable Data Generation for Evaluating Mixed-Precision Solvers. In 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020 Article 9286145 (2020 IEEE High Performance Extreme Computing Conference, HPEC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPEC43674.2020.9286145

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title = "Scalable Data Generation for Evaluating Mixed-Precision Solvers",

abstract = "We present techniques of generating data for mixed precision solvers that allows to test those solvers in a scalable manner. Our techniques focus on mixed precision hardware and software where both the solver and the hardware can take advantage of mixing multiple floating precision formats. This allows taking advantage of recently released generation of hardware platforms that focus on ML and DNN workloads but can also be utilized for HPC applications if a new breed of algorithms is combined with the custom floating-point formats to deliver performance levels beyond the standard IEEE data types while delivering a comparable accuracy of the results.",

author = "Piotr Luszczek and Yaohung Tsai and Neil Lindquist and Hartwig Anzt and Jack Dongarra",

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doi = "10.1109/HPEC43674.2020.9286145",

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Luszczek, P, Tsai, Y, Lindquist, N, Anzt, H & Dongarra, J 2020, Scalable Data Generation for Evaluating Mixed-Precision Solvers. in 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020., 9286145, 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020, Institute of Electrical and Electronics Engineers Inc., 2020 IEEE High Performance Extreme Computing Conference, HPEC 2020, Virtual, Waltham, United States, 09/21/20. https://doi.org/10.1109/HPEC43674.2020.9286145

Scalable Data Generation for Evaluating Mixed-Precision Solvers. / Luszczek, Piotr; Tsai, Yaohung; Lindquist, Neil et al.
2020 IEEE High Performance Extreme Computing Conference, HPEC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9286145 (2020 IEEE High Performance Extreme Computing Conference, HPEC 2020).

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

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Scalable Data Generation for Evaluating Mixed-Precision Solvers

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