Automatic parallelism tuning for module learning with errors based post-quantum key exchanges on GPUs

Tatsuki Ono; Song Bian; Takashi Sato

doi:10.1109/ISCAS51556.2021.9401575

Automatic parallelism tuning for module learning with errors based post-quantum key exchanges on GPUs

Tatsuki Ono
, Song Bian
, Takashi Sato

Kyoto University

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

6 Scopus citations

Abstract

The module learning with errors (MLWE) problem is one of the most promising candidates for constructing quantum-resistant cryptosystems. In this work, we propose an open-source framework to automatically adjust the level of parallelism for MLWE-based key exchange protocols to maximize the protocol execution efficiency. We observed that the number of key exchanges handled by primitive functions in parallel, and the dimension of the grids in the GPUs have significant impacts on both the latencies and throughputs of MLWE key exchange protocols. By properly adjusting the related parameters, in the experiments, we show that performance of MLWE based key exchange protocols can be improved across GPU platforms.

Original language	English
Title of host publication	2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728192017
DOIs	https://doi.org/10.1109/ISCAS51556.2021.9401575
State	Published - 2021
Externally published	Yes
Event	53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Daegu, Korea, Republic of Duration: 22 May 2021 → 28 May 2021

Publication series

Name	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	2021-May
ISSN (Print)	0271-4310

Conference

Conference	53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021
Country/Territory	Korea, Republic of
City	Daegu
Period	22/05/21 → 28/05/21

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10.1109/ISCAS51556.2021.9401575

Cite this

Ono, T., Bian, S., & Sato, T. (2021). Automatic parallelism tuning for module learning with errors based post-quantum key exchanges on GPUs. In 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings Article 9401575 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2021-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISCAS51556.2021.9401575

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title = "Automatic parallelism tuning for module learning with errors based post-quantum key exchanges on GPUs",

abstract = "The module learning with errors (MLWE) problem is one of the most promising candidates for constructing quantum-resistant cryptosystems. In this work, we propose an open-source framework to automatically adjust the level of parallelism for MLWE-based key exchange protocols to maximize the protocol execution efficiency. We observed that the number of key exchanges handled by primitive functions in parallel, and the dimension of the grids in the GPUs have significant impacts on both the latencies and throughputs of MLWE key exchange protocols. By properly adjusting the related parameters, in the experiments, we show that performance of MLWE based key exchange protocols can be improved across GPU platforms.",

author = "Tatsuki Ono and Song Bian and Takashi Sato",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021 ; Conference date: 22-05-2021 Through 28-05-2021",

year = "2021",

doi = "10.1109/ISCAS51556.2021.9401575",

language = "英语",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings",

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}

Ono, T, Bian, S & Sato, T 2021, Automatic parallelism tuning for module learning with errors based post-quantum key exchanges on GPUs. in 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings., 9401575, Proceedings - IEEE International Symposium on Circuits and Systems, vol. 2021-May, Institute of Electrical and Electronics Engineers Inc., 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021, Daegu, Korea, Republic of, 22/05/21. https://doi.org/10.1109/ISCAS51556.2021.9401575

Automatic parallelism tuning for module learning with errors based post-quantum key exchanges on GPUs. / Ono, Tatsuki; Bian, Song; Sato, Takashi.
2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021. 9401575 (Proceedings - IEEE International Symposium on Circuits and Systems; Vol. 2021-May).

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

TY - GEN

T1 - Automatic parallelism tuning for module learning with errors based post-quantum key exchanges on GPUs

AU - Ono, Tatsuki

AU - Bian, Song

AU - Sato, Takashi

PY - 2021

Y1 - 2021

N2 - The module learning with errors (MLWE) problem is one of the most promising candidates for constructing quantum-resistant cryptosystems. In this work, we propose an open-source framework to automatically adjust the level of parallelism for MLWE-based key exchange protocols to maximize the protocol execution efficiency. We observed that the number of key exchanges handled by primitive functions in parallel, and the dimension of the grids in the GPUs have significant impacts on both the latencies and throughputs of MLWE key exchange protocols. By properly adjusting the related parameters, in the experiments, we show that performance of MLWE based key exchange protocols can be improved across GPU platforms.

AB - The module learning with errors (MLWE) problem is one of the most promising candidates for constructing quantum-resistant cryptosystems. In this work, we propose an open-source framework to automatically adjust the level of parallelism for MLWE-based key exchange protocols to maximize the protocol execution efficiency. We observed that the number of key exchanges handled by primitive functions in parallel, and the dimension of the grids in the GPUs have significant impacts on both the latencies and throughputs of MLWE key exchange protocols. By properly adjusting the related parameters, in the experiments, we show that performance of MLWE based key exchange protocols can be improved across GPU platforms.

UR - https://www.scopus.com/pages/publications/85109024543

U2 - 10.1109/ISCAS51556.2021.9401575

DO - 10.1109/ISCAS51556.2021.9401575

M3 - 会议稿件

AN - SCOPUS:85109024543

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

BT - 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021

Y2 - 22 May 2021 through 28 May 2021

ER -

Ono T, Bian S, Sato T. Automatic parallelism tuning for module learning with errors based post-quantum key exchanges on GPUs. In 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021. 9401575. (Proceedings - IEEE International Symposium on Circuits and Systems). doi: 10.1109/ISCAS51556.2021.9401575

Automatic parallelism tuning for module learning with errors based post-quantum key exchanges on GPUs

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