GOLF: Unleashing GPU-Driven Acceleration for FALCON Post-Quantum Cryptography

Quantum computers leverage qubits to solve certain computational problems significantly faster than classical computers. This capability poses a severe threat to traditional cryptographic algorithms, leading to the rise of post-quantum cryptography (PQC) designed to withstand quantum attacks. FALCON, a lattice-based signature algorithm, has been selected by the National Institute of Standards and Technology (NIST) as part of its post-quantum cryptography standardization process. However, due to the computational complexity of PQC, especially in cloud-based environments, throughput limitations during peak demand periods have become a bottleneck, particularly for FALCON. In this paper, we introduce GOLF (GPU-accelerated Optimization for Lattice-based FALCON), a novel GPU-based parallel acceleration framework for FALCON. GOLF includes algorithm porting to the GPU, compatibility modifications, multi-threaded parallelism with distinct data, single-thread optimization for single tasks, and specific enhancements to the Fast Fourier Transform (FFT) module within FALCON. Our approach achieves unprecedented performance in FALCON acceleration on GPUs, setting the highest throughput record in the history of FALCON digital signature generation and verification. On the NVIDIA RTX 4090, GOLF reaches a signature generation throughput of 420.25 kops/s and a signature verification throughput of 10,311.04 kops/s. These results represent a $58.05\times $ / $73.14\times $ improvement over the reference FALCON implementation and a $7.17\times $ / $3.79\times $ improvement compared to the fastest known GPU implementation to date. Additionally, since we have not modified the content of the algorithm, but only optimized its engineering implementation, the security of the algorithm has not changed, and the security of the original algorithm has been maintained. GOLF demonstrates that GPU acceleration is not only feasible for post-quantum cryptography but also crucial for addressing throughput bottlenecks in real-world applications.