Paper 2024/1030

GRASP: Accelerating Hash-based PQC Performance on GPU Parallel Architecture

Abstract

$$, one of the Post-Quantum Cryptography Digital Signature Algorithms (PQC-DSA) selected by NIST in the third round, features very short public and private key lengths but faces significant performance challenges compared to other post-quantum cryptographic schemes, limiting its suitability for real-world applications. To address these challenges, we propose the GPU-based paRallel Accelerated $$ (GRASP), which leverages GPU technology to enhance the efficiency of $$ signing and verification processes. We propose an adaptable parallelization strategy for $$, analyzing its signing and verification processes to identify critical sections for efficient parallel execution. Utilizing CUDA, we perform bottom-up optimizations, focusing on memory access patterns and hypertree computation, to enhance GPU resource utilization. These efforts, combined with kernel fusion technology, result in significant improvements in throughput and overall performance. Extensive experimentation demonstrates that our optimized CUDA implementation of $$ achieves superior performance. Specifically, our GRASP scheme delivers throughput improvements ranging from 1.37× to 3.45× compared to state-of-the-art GPU-based solutions and surpasses the NIST reference implementation by over three orders of magnitude, highlighting a significant performance advantage.