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赖于详细的模拟，以避免昂贵的制造过程。然而，计算机架构模拟的速度没有跟上计算技术的快速发展，特别是对于执行具有大计算、大存储器需求和大通信需求的应用的系统。该项目引入了创新的轻量级仿真技术，通过选择性地仿真芯片设计的某些方面或使用更高级别的抽象来提高效率，从而大大加快仿真过程。该项目将使研究界能够获得支持更快开发新计算技术的技术。研究成果将使计算机体系结构领域的研究人员更容易获得更少的资源。此外，从这个项目中衍生的模拟技术将被整合到计算机体系结构课程中，帮助学生，特别是资源不足的学生，更好地理解与大规模计算相关的概念。传统的计算机体系结构模拟器重新创建硬件执行的逐周期细节，阻碍了快速模拟。为了提高性能，该项目引入了一套新颖的仿真工具，旨在支持下一代大规模计算系统的设计和优化。该方法包括三个互补的策略：行为建模，采样模拟和数据驱动的模拟。行为建模将硬件组件抽象化，以专注于基本性能指标，从而实现更快的模拟，而不会显著降低准确性。抽样模拟利用应用程序的重复性（特别关注GPU应用程序），通过仅模拟工作负载的关键部分来预测性能。数据驱动的仿真利用统计和性能建模技术进一步提高仿真能力。这些策略将统一在秋田模拟器框架下，促进不同模拟方案的互操作性和易用性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。