SIMD-Accelerated Optimization of Runs Test Performance on Binary Sequences

The runs test, as one of the basic binary sequence randomness analysis methods, has been widely adopted in various international standards and industry. However, existing runs test schemes suffer from critical performance limitations, such as large lookup tables to calculate Hamming weight, causing cache inefficiency; data loading redundancy in computing Hamming weight. We propose a SIMD-accelerated optimization scheme to enhance the runs test performance. This scheme includes: (1) replacing software lookup tables with SIMD bit-counting instructions to optimize cache efficiency; (2) designing a parallel computing algorithm that consolidates the dual traversals for computing Hamming weight into a single traversal, to eliminate redundant data loading. The proposed scheme exhibits architectural independence, as it fully achieves performance improvements on x86 and ARM architectures. Notably, the proposed scheme achieves 161,134.39[Formula: see text]Mbps detection speed, that is 16.98 times compared to the current Fast NIST STS implementation.

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Publication Details

Journal
International Journal of Pattern Recognition and Artificial Intelligence
Published
2026-01-14
DOI
https://doi.org/10.1142/s0218001426500011
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
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article

SIMD-Accelerated Optimization of Runs Test Performance on Binary Sequences

Hongjuan Kang, Na Shi, Bing Guo, Ying Luo
International Journal of Pattern Recognition and Artificial Intelligence
Parallel Computing and Optimization Techniques
article

SIMD-Accelerated Optimization of Runs Test Performance on Binary Sequences

Hongjuan Kang, Na Shi, Bing Guo, Ying Luo
article en

Abstract

The runs test, as one of the basic binary sequence randomness analysis methods, has been widely adopted in various international standards and industry. However, existing runs test schemes suffer from critical performance limitations, such as large lookup tables to calculate Hamming weight, causing cache inefficiency; data loading redundancy in computing Hamming weight. We propose a SIMD-accelerated optimization scheme to enhance the runs test performance. This scheme includes: (1) replacing software lookup tables with SIMD bit-counting instructions to optimize cache efficiency; (2) designing a parallel computing algorithm that consolidates the dual traversals for computing Hamming weight into a single traversal, to eliminate redundant data loading. The proposed scheme exhibits architectural independence, as it fully achieves performance improvements on x86 and ARM architectures. Notably, the proposed scheme achieves 161,134.39[Formula: see text]Mbps detection speed, that is 16.98 times compared to the current Fast NIST STS implementation.

International Journal of Pattern Recognition and Artificial Intelligence
Sichuan University (CN), Beijing Founder Electronics (China) (CN), Changhong (China) (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 94%
Parallel Computing and Optimization Techniques
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