GPU-based acceleration technology for signature verification of blockchain transactions
Can CUI1(),Xiao-hu YANG1,*(),Wei-wei QIU2,Fang-lei HUANG2
1. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China 2. Hangzhou Qulian Technology Co. Ltd, Hangzhou 310000, China
A GPU-based acceleration technology for signature verification of blockchain transactions was proposed, in order to improve the signature verification efficiency of peers. A phased optimization for signature verification process of blockchain transactions was performed by combining the characteristics of CPU-GPU heterogeneous platform architecture, greatly improving the efficiency of the SM2 verification algorithm. The asynchronous nature of GPU kernel calls was fully utilized, effectivelly reducing the overall IO overhead of the transaction signature verification process. Considering the characteristics of GPU with strong computational power and weak branch prediction, an improved simultaneous multipoint multiplication algorithm was proposed, which not only improved the efficiency of GPU signature verification, but also increased the multi-threaded parallelism scale. The proposed method offloaded the transaction signature verification operation to GPU processing, which freed up the CPU resources of peers occupied and improved the overall performance of the blockchain system without modifying the blockchain protocols. Experimental results based on the RTX3080 platform and Hyperchain, a domestic permissioned blockchain, showed that the peak signature verification throughput of the proposed method was 4.52×106 transactions per second, and the transaction throughput of Hyperchain platform integrated with the proposed method increased by 15.81% while latency decreased by 6.56%.
Can CUI,Xiao-hu YANG,Wei-wei QIU,Fang-lei HUANG. GPU-based acceleration technology for signature verification of blockchain transactions. Journal of ZheJiang University (Engineering Science), 2023, 57(8): 1505-1515.
Fig.1Framework for GPU-based acceleration technology for signature verification of blockchain transactions
Fig.2Column-major fetch mode
Fig.3Throughput and latency of signature verification acceleration technology
优化阶段
是否优化
${T_{\text{P}}}$/（105 次·s?1）
${L_{\text{P}}}$/ms
$\lambda $/%
数据组织与传输
优化
45.2
38.03
11.06
未优化
40.7
34.33
GPU多线程 并行验签
优化
39.6
28.91
2490.00
未优化
1.59
1080
结果更新与确认
优化
45.2
38.03
3.91
未优化
43.5
36.09
Tab.1Evaluation of optimization effects of each stage
Fig.4Throughput comparison of Euclid and Fermat signature verification solutions
Fig.5Experimental design of Euclid and Fermat computational solutions
Fig.6Comparison of peak throughput of Euclid and Fermat computing solutions
Fig.7Comparison of peak throughput of simultaneous multipoint multiplication and standard multipoint multiplication
实验方案
${T_{\text{O}}}$/（次·s?1）
${L_{\text{B}}}$/ms
Hyperchain（使用本研究方案）
45410
58.42
Hyperchain（不使用本研究方案）
39209
62.52
Tab.2Validation of effect for integrated optimization
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