Cryptology ePrint Archive: Report 2021/198

Automatic Parallelism Tuning for Module Learning with Errors Based Post-Quantum Key Exchanges on GPUs

Tatsuki Ono and Song Bian and Takashi Sato

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.

Category / Keywords: implementation / Automatic parameter tuning, GPU, high-performance computing, Module Learning with Errors, LWE, Post-Quantum Cryptography, lattice cryptography

Original Publication (in the same form): IEEE International Symposium on Circuit and Systems (ISCAS) 2021

Date: received 23 Feb 2021

Contact author: paper at easter kuee kyoto-u ac jp

Available format(s): PDF | BibTeX Citation

Version: 20210224:145600 (All versions of this report)

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