Algorithm Research & Explore
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1735-1742

Parallel computation method for multi-scalar multiplication in zk-SNARK based on GPU

Wang Feng1a
Chai Zhilei1a,2
Hua Pengcheng1a
Ding Dong1b
Wang Ning1a
1. a. School of Artificial Intelligence & Computer Science, b. School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China
2. Jiangsu Provincial Engineering Laboratory of Pattern Recognition & Computational Intelligence, Wuxi Jiangsu 214122, China

Abstract

In the context of zk-SNARK, MSM emerges as the predominant computational bottleneck. To address this problem, this paper proposed a GPU-based parallel MSM computation approach. Firstly, the method performed fine-grained task decomposition of MSM to enhance algorithmic computational parallelism, fully leveraging the extensive parallel computing capabilities of GPU. Additionally, it reduced data transfer overhead by employing shared memory for the parallel reduction of sub-MSM tasks within the same window. Secondly, the method introduced a window partitioning strategy based on thread-level task load analysis of the underlying computational modules to search for the optimal scalar window, thereby minimizing the computational cost of MSM subtasks. Lastly, the method optimized the data storage structure used for scalar form transformation and mitigated latency issues in the large-scale scalar form conversion process by employing data overlap transfer and hidden communication time. This paper implemented the MSM parallel computation method based on CUDA on NVIDIA GPU and established a comprehensive zero-knowledge proof heterogeneous computing system. Experimental results show that the proposed method achieves an acceleration ratio of 1.38 times compared to the current state-of-the-art MSM calculation module of cuZK. The overall system based on the improved MSM is 186 times better than the industry-popular Bellman, and 1.96 times better than cutting edge heterogeneous version Bellperson, validating the effectiveness of the approach.

Foundation Support

国家自然科学基金资助项目(61972180)
江苏省模式识别与计算智能工程实验室项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0498
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 6
Section: Algorithm Research & Explore
Pages: 1735-1742
Serial Number: 1001-3695(2024)06-019-1735-08

Publish History

[2024-02-02] Accepted Paper
[2024-06-05] Printed Article

Cite This Article

王锋, 柴志雷, 花鹏程, 等. 基于GPU的zk-SNARK中多标量乘法的并行计算方法 [J]. 计算机应用研究, 2024, 41 (6): 1735-1742. (Wang Feng, Chai Zhilei, Hua Pengcheng, et al. Parallel computation method for multi-scalar multiplication in zk-SNARK based on GPU [J]. Application Research of Computers, 2024, 41 (6): 1735-1742. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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