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

Tatsuki Ono; Song Bian; Takashi Sato

Paper 2021/198

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

Tatsuki Ono, 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.

Metadata

Available format(s): PDF
Category: Implementation
Publication info: Published elsewhere. IEEE International Symposium on Circuit and Systems (ISCAS) 2021
Keywords: Automatic parameter tuning GPU high-performance computing Module Learning with Errors LWE Post-Quantum Cryptography lattice cryptography
Contact author(s): paper @ easter kuee kyoto-u ac jp
History: 2021-02-24: received
Short URL: https://ia.cr/2021/198
License: CC BY

BibTeX

@misc{cryptoeprint:2021/198,
      author = {Tatsuki Ono and Song Bian and Takashi Sato},
      title = {Automatic Parallelism Tuning for Module Learning with Errors Based Post-Quantum Key Exchanges on GPUs},
      howpublished = {Cryptology ePrint Archive, Paper 2021/198},
      year = {2021},
      note = {\url{https://eprint.iacr.org/2021/198}},
      url = {https://eprint.iacr.org/2021/198}
}