CBPSPX: A CUDA-Based Batch Parallel Optimization of Post-Quantum Signature SPHINCS+
Jiafei Wu, Yifei Yu, Zhao Chen, Hao Yang, Chao Li, Zhe Liu · IEEE Internet of Things Journal · 2025
Security and privacy are critical in cloud-based Internet of Things (IoT) and Artificial Intelligence of Things (AIoT) applications. As quantum computing advances, Post-Quantum Cryptography (PQC) has emerged as a key technology for ensuring security in future IoT and AIoT architectures. SPHINCS+, a leading post-quantum signature algorithm, has been selected by the National Institute of Standards and Technology (NIST) as one of the next-generation signature standards. However, due to its complex structure and extensive hash operations, SPHINCS+ suffers from slower signature generation and verification compared to other post-quantum algorithms. Consequently, accelerating SPHINCS+ is essential for adapting it to IoT environments. This paper presents a CUDA-based Batch Parallel optimization of SPHINCS+ (CBPSPX), which fully utilizes the computing resources of NVIDIA Graphics Processing Units (GPUs) to enhance the performance of SPHINCS+. Specifically, we propose the Thread Utilization Efficiency Index (TUEI), which can be used to theoretically evaluate the effectiveness of various parallel methods. Then, we propose an intra-block batch processing model that dynamically adjusts parallel task scales within a block to optimize throughput, making it particularly suitable for IoT scenarios requiring high-throughput large-scale device authentication. Meanwhile, we divide the signature generation process into three sub-processes and adopt different parallel strategies based on the thread requirements of each sub-process to maximize the value of TUEI. For the signature verification process, we propose a columnar storage strategy to replace the traditional row storage structure, which significantly improves the performance of batch signature verification. Experimental results indicate that our SPHINCS+ implementations across all three parameter sets are better than previous optimized GPU-based implementations and achieve speedups of 1.4× to 2.5× for signature generation and 4.6× to 11.3× for signature verification on GPU RTX 3090.