[Resource Topic] 2023/1522: cuML-DSA: Optimized Signing Procedure and Server-Oriented GPU Design for ML-DSA

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

Authors: Shiyu Shen, Hao Yang, Wenqian Li, Yunlei Zhao


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.

ePrint: https://eprint.iacr.org/2023/1522

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