GPGPU微体系结构模拟是其硬件架构设计与软件应用开发的重要评估测试技术。然而，由于GPGPU软硬件的庞大与复杂性，其微体系结构模拟速度缓慢，评估测试周期漫长，严重制约设计开发效率。本项目针对这一问题，主要研究面向GPGPU微体系结构模拟的采样加速理论与策略。主要研究内容分为循序渐进的三部分：首先，研究刻画GPGPU程序运行行为特征表示方法，提出分析行为特征理论模型，为采样模型建立与策略设计提供理论指导；其次，基于GPGPU行为特征的分析归纳，开展契合GPGPU环境的分层采样模拟加速理论研究与策略设计；再次，构建采样模拟效率评估模型，研究影响模拟精度与速度的重要因素，并根据评估结果与影响因素反馈优化采样模拟加速理论模型与策略。最终，本项目提供一种面向GPGPU微体系结构模拟的采样加速理论与方法，在保持模拟精度前提下，大幅缩短模拟评估周期（模拟时长平均减少10倍以上），显著提高模拟评估效率。

Micro-architectural simulation is the key technology to evaluate GPGPU’s hardware design and software development. However, the simulation speed is extremely slow due to its hardware and software complexities, which reduce the efficiency of design and development tremendously. To address this issue, this project conducts the research to accelerate GPGPU’s micro-architectural simulation and improve its evaluation efficiency through sampling theory. Firstly, by observing the inherent runtime characteristics of GPGPU benchmarks, we propose a model with reliable metrics to measure the runtime behavior. Secondly, we give a hybrid sampling methodology to speedup GPGPU’s micro-architectural simulation based on the detailed analysis of GPGPU benchmarks runtime behavior. Then, we discuss the important factors that influence simulation speed and accuracy. Moreover, we perform detailed evaluation for the proposed sampling methodology, and make further improvements guided by the analysis of experimental results. At last, this project will provide a sampling theory and strategy for GPGPU micro-architectural simulation, reduce the simulation time by a factor of 10x on average, and significantly improve GPGPU’s design and development efficiency.