Collaborative Research: SHF: Medium: Enabling GPU Performance Simulation for Large-Scale Workloads with Lightweight Simulation Methods

合作研究：SHF：中：通过轻量级仿真方法实现大规模工作负载的 GPU 性能仿真

• 批准号: 2402806
• 负责人: Sreepathi Pai
• 金额: $ 37.77万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2402806&HistoricalAwards=false
• 关键词: Collaborative; Research; SHF; Medium; Enabling

Designing the architecture of new computer chips typically relies on detailed simulations to avoid expensive manufacturing processes. However, the speed of computer architecture simulations has not kept up with the rapid advancements in computing technology, particularly for systems that execute applications with large computations, large memory requirements, and large communication needs. This project introduces innovative lightweight simulation techniques that focus on efficiency by selectively simulating certain aspects of chip design or using higher levels of abstraction, drastically speeding up the simulation process. This project will enable the research community with techniques that support quicker development of new computing technologies. The research outcome will make the field of computer architecture more accessible to researchers with fewer resources. Moreover, the simulation techniques derived from this project will be integrated into the computer architecture curricula, helping students, especially under-resourced students, better understand concepts related to large-scale computing. Traditional computer architecture simulators recreate cycle-by-cycle details of the hardware execution, hindering fast simulation. To improve performance, this project introduces a novel suite of simulation tools designed to support the design and optimization of next-generation, large-scale computing systems. The approach encompasses three complementary strategies: behavior modeling, sampled simulations, and data-driven simulation. Behavior modeling abstracts hardware components to focus on essential performance metrics, enabling faster simulations without significant loss of accuracy. Sampled simulations leverage the repetitive nature of applications (with a special focus on GPU applications) to predict performance by simulating only critical segments of the workload. Data-driven simulations take advantage of statistical and performance modeling techniques to further advance simulation capabilities. These strategies will be unified under the Akita simulator framework, facilitating interoperability and ease of use across different simulation schemes.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

设计新计算机芯片的架构通常依靠详细的模拟来避免昂贵的制造过程。但是，计算机体系结构模拟的速度并没有跟上计算技术的快速进步，尤其是对于执行具有较大计算，庞大内存需求和较大通信需求的应用程序的系统。该项目引入了创新的轻质模拟技术，通过选择性模拟芯片设计的某些方面或使用更高级别的抽象，从而大大加快了模拟过程，从而将重点放在效率上。该项目将通过支持更快开发新计算技术的技术使研究社区能够。研究结果将使计算机架构的领域更容易被资源较少的研究人员访问。此外，该项目得出的仿真技术将集成到计算机体系结构课程中，帮助学生，尤其是资源不足的学生，更好地了解与大规模计算有关的概念。传统的计算机体系结构模拟器逐行循环硬件执行的详细信息，阻碍了快速模拟。为了提高性能，该项目推出了一套新颖的仿真工具套件，旨在支持下一代大规模计算系统的设计和优化。该方法包括三种互补策略：行为建模，采样模拟和数据驱动模拟。行为建模摘要硬件组件专注于基本的性能指标，从而可以更快地模拟，而不会明显丧失准确性。采样模拟利用应用程序的重复性质（特别关注GPU应用程序）来通过仅模拟工作量的关键段来预测性能。数据驱动的模拟利用统计和性能建模技术来进一步提高模拟功能。这些策略将在Akita Simulator框架下统一，从而促进互操作性和易于使用的不同模拟方案。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来进行评估。