CAREER: GPU Performance Portability for Volunteer Computing through Heterogeneity-aware Autotuning

职业：通过异构感知自动调整实现志愿计算的 GPU 性能可移植性

• 批准号: 2144384
• 负责人: Sreepathi Pai
• 金额: $ 49.14万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2144384&HistoricalAwards=false
• 关键词: CAREER; GPU; Performance; Portability; Volunteer

This award is funded in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Computational power is increasingly critical for discovering phenomena such as gravitational waves and to accelerate the discovery of new drugs such as vaccines. Graphics Processing Units (GPUs) are computational devices that are frequently used to speed up such scientific calculations. To achieve maximum performance, software must be tuned to the particular GPU being used. Although such automatic performance tuning (autotuning) is routine and effective in conventional supercomputing environments where all the GPUs are identical, the process does not scale well or extract maximum performance in environments containing hundreds of different types of GPUs. Such heterogeneity is common in volunteer computing where donated computer time is used to perform massive computations. By developing autotuning algorithms that can deal with heterogeneity, providing feedback to programmers about unrealized performance, and working at scale in real volunteer computing systems, this project will enable these systems to maximize GPU performance and accelerate scientific discovery in fields such as medicine, biology, and astronomy. The goal of this project is to automatically adapt applications to obtain maximal performance on all GPUs in the highly heterogeneous volunteer computing environment at lower cost than re-running autotuning on each GPU encountered. First, methods to identify similarities among GPUs are developed to reduce autotuning effort while performance data gathered across different GPUs is used to bootstrap and speed up the autotuning process. Second, performance models are integrated with autotuning to provide feedback to programmers about bottlenecks and missed performance optimization opportunities. Third, parallel and distributed search is used to prune unproductive explorations of an application’s performance landscape. All these will be implemented in the popular real-world BOINC volunteer computing system to benefit both existing and future scientific volunteer computing projects. The project will conduct outreach activities for K-12 students, and also aid the preservation of electronic games by providing hi-fidelity GPU models.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.

该奖项是根据2021年《美国救援计划法》（公共法第117-2）的一部分资助的。统计权力对于发现等现象（例如引力波）和加速新药（如疫苗）而言至关重要。图形处理单元（GPU）是经常用于加快此类科学计算的计算设备。为了实现最高性能，必须将软件调整为特定的GPU，尽管这种自动性能调整（自动调整）是常规的，并且在所有GPU相同的传统超级计算环境中是常规的，并且在包含数百种不同类型的GPU的环境中缩放或提取最大性能。这种异质性在志愿者计算中很常见，其中捐赠的计算机时间用于执行大规模计算。通过开发可以处理异质性，向程序员提供有关未实现性能的反馈以及在实际志愿计算系统中大规模工作的自动调整算法，该项目将使这些系统能够最大程度地提高GPU性能并在医学，生物学和天文学等领域中最大化科学发现。该项目的目的是自动调整应用程序，以在高度异构志愿者计算环境中以低成本的价格获得所有GPU的最高性能，而不是每次遇到的GPU上的自动调整。首先，开发了识别GPU之间相似之处的方法，以减少自动汇率的工作，而在不同GPU中收集的性能数据则用于引导和加快自动调节过程。其次，性能模型与自动调整集成在一起，以向程序员提供有关瓶颈和遗漏性能优化机会的反馈。第三，并行和分布式搜索用于修剪应用程序性能景观的非生产性探索。所有这些都将在流行的现实BOINC志愿计算系统中实施，以使现有和未来的科学志愿者计算项目受益。该项目将为K-12学生开展外展活动，并通过提供高保真性GPU模型来帮助保存电子游戏。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子的评估和更广泛的影响来通过评估来支持的。