一句话：提出面向多 GPU 的 MSM 算法 DistMSM，通过算法适配与 GPU 内核优化，将零知识证明的 MSM 加速平均 6.39 倍。
零知识证明中证明生成时间过长，即使 GPU 加速仍需数分钟，其中多标量乘法（MSM）约占 78.2% 工作量。本文提出 DistMSM，一种面向分布式多 GPU 系统的 MSM 算法。算法层将 Pippenger 算法适配到多 GPU 环境，识别并解决扩展时的瓶颈；GPU 内核层设计面向现代 GPU 架构的椭圆曲线算术内核，利用两项技术优化寄存器压力，并借助张量核心完成特定大整数乘法。与最先进的 MSM 实现相比，DistMSM 在不同椭圆曲线和 GPU 数量下平均加速 6.39 倍，原先单 GPU 需数秒的 MSM 任务可缩短至数十毫秒，展示了多 GPU 系统加速 ZKP 的潜力。
Zero-knowledge proof is a cryptographic primitive that allows for the validation of statements without disclosing any sensitive information, foundational in applications like verifiable outsourcing and digital currency. However, the extensive proof generation time limits its widespread adoption. Even with GPU acceleration, proof generation can still take minutes, with Multi-Scalar Multiplication (MSM) accounting for about 78.2% of the workload. To address this, we present DistMSM, a novel MSM algorithm tailored for distributed multi-GPU systems. At the algorithmic level, DistMSM adapts Pippenger's algorithm for multi-GPU setups, effectively identifying and addressing bottlenecks that emerge during scaling. At the GPU kernel level, DistMSM introduces an elliptic curve arithmetic kernel tailored for contemporary GPU architectures. It optimizes register pressure with two innovative techniques and leverages tensor cores for specific big integer multiplications. Compared to state-of-the-art MSM implementations, DistMSM offers an average 6.39× speedup across various elliptic curves and GPU counts. An MSM task that previously took seconds on a single GPU can now be completed in mere tens of milliseconds. It showcases the substantial potential and efficiency of distributed multi-GPU systems in ZKP acceleration.