Simulation tools are critical for architects to quickly estimate the impact of aggressive new features of GPU architecture. Existing cycle-accurate GPU simulators are typically cumbersome and slow to run. We observe that it is time-consuming and unnecessary for cycle-accurate GPU simulators to perform detailed simulations for the entire GPU when exploring the design space of specific components. This paper proposes Swift-Sim, a modular and hybrid GPU simulation framework. With a highly modular design, our framework can choose appropriate modeling approaches for each component according to requirements. For components of interest to architects, we use cycle-accurate simulation to evaluate new GPU architectures. For other components, we use analytical modeling, which accelerates simulation speed with only minor and acceptable degradation in overall accuracy. Based on this simulation framework, we present two working examples of hybrid modeling that simulate the ALU pipeline and memory accesses using analytical models. We further implement two GPU performance simulators with different levels of simplification based on Swift-Sim and evaluate them using configurations from real GPUs. The results show that the two simulators achieve an 82.6x and 211.2x geometric mean speedup compared to Accel-Sim with insignificant accuracy degradation,

Zero-knowledge proof (ZKP) is a popular cryptographic strategy for building a trusted environment, which can be applied to blockchain, electronic voting, and other scenarios. However, ZKP involves a number of computationally intensive operations that limit its widespread adoption in time-sensitive practical applications. The multi-scalar multiplication (MSM) dominates the computations and takes over 70% of the total computation time. This paper proposes a GPU-based acceleration method for ZKP by designing several optimization techniques for MSM. First, this paper constructs a formal mathematical formula of the Pippenger algorithm, which provides a theoretical optimization framework for MSM. Second, by parallelizing the prefix sum, the time complexity of the bucket reduction part of MSM is reduced from $\mathcal{O}\left( {3 \times {2^C}} \right)$ to $\mathcal{O}\left( {2 \times {2^C}} \right)$. Finally, this paper also analyzes the influence of group size on the final calculation time under different data scales and gives a suitable range of group sizes. Compared to the state-of-the-art method, our method can achieve 1.01× to 1.12× for throughput.