Paper 2023/1522

cuML-DSA: Optimized Signing Procedure and Server-Oriented GPU Design for ML-DSA

Shiyu Shen, Fudan University
Hao Yang, Nanjing University of Aeronautics and Astronautics
Wenqian Li, Fudan University
Yunlei Zhao, Fudan University
Abstract

The threat posed by quantum computing has precipitated an urgent need for post-quantum cryptography. Recently, the post-quantum digital signature draft FIPS 204 has been published, delineating the details of the ML-DSA, which is derived from the CRYSTALS-Dilithium. Despite these advancements, server environments, especially those equipped with GPU devices necessitating high-throughput signing, remain entrenched in classical schemes. A conspicuous void exists in the realm of GPU implementation or server-specific designs for ML-DSA. In this paper, we propose the first server-oriented GPU design tailored for the ML-DSA signing procedure in high-throughput servers. We introduce several innovative theoretical optimizations to bolster performance, including depth-prior sparse ternary polynomial multiplication, the branch elimination method, and the rejection-prioritized checking order. Furthermore, exploiting server-oriented features, we propose a comprehensive GPU hardware design, augmented by a suite of GPU implementation optimizations to further amplify performance. Additionally, we present variants for sampling sparse polynomials, thereby streamlining our design. The deployment of our implementation on both server-grade and commercial GPUs demonstrates significant speedups, ranging from 170.7× to 294.2× against the CPU baseline, and an improvement of up to 60.9% compared to related work, affirming the effectiveness and efficiency of the proposed GPU architecture for ML-DSA signing procedure.

Metadata
Available format(s)
PDF
Category
Implementation
Publication info
Preprint.
Keywords
post-quantum cryptographydigital signatureML-DSAsparse polynomial multiplicationGPU acceleration
Contact author(s)
shenshiyu21 @ m fudan edu cn
crypto @ d4rk dev
22210240090 @ m fudan edu cn
ylzhao @ fudan edu cn
History
2023-10-06: approved
2023-10-06: received
See all versions
Short URL
https://ia.cr/2023/1522
License
Creative Commons Attribution
CC BY

BibTeX

@misc{cryptoeprint:2023/1522,
      author = {Shiyu Shen and Hao Yang and Wenqian Li and Yunlei Zhao},
      title = {{cuML}-{DSA}: Optimized Signing Procedure and Server-Oriented {GPU} Design for {ML}-{DSA}},
      howpublished = {Cryptology {ePrint} Archive, Paper 2023/1522},
      year = {2023},
      url = {https://eprint.iacr.org/2023/1522}
}
Note: In order to protect the privacy of readers, eprint.iacr.org does not use cookies or embedded third party content.