cuFalcon: An Adaptive Parallel GPU Implementation for High-Performance Falcon Acceleration

The rapid advancement of quantum computing has ushered in a new era of post-quantum cryptography, urgently demanding quantum-resistant digital signatures to secure modern communications and transactions. Among NIST-standardized candidates, Falcon stands out because it is a compact lattice-based signature scheme suitable for size-sensitive applications. In this paper, we present cuFalcon, a high-throughput GPU implementation of Falcon that addresses its computational bottlenecks through adaptive parallel strategies. At the operational level, we optimize Falcon key components for GPU architectures through memory-efficient FFT, adaptive parallel ffSampling, and a compact computation mode. For signature-level optimization, to improve scalability across different GPU architectures, we implement three versions of cuFalcon: the raw key version, the expanded key version, and the balanced version. Additionally, we design batch processing, streaming mechanisms, and memory pooling to handle multiple signature tasks efficiently. Ultimately, performance evaluations show significant improvements, with the raw key version achieving 172 k signatures per second and the expanded key version reaching 201 k. Compared to the raw key version, the balanced version achieves a 7% improvement in throughput, while compared to the expanded key version, it reduces memory usage by 70%. Furthermore, our raw key version implementation outperforms the reference implementation by 36.74 × and achieves a 2.71× speedup over the state-of-the-art GPU implementation.