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.

该奖项的部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。计算能力对于发现引力波等现象以及加速图形处理单元等新药物的发现变得越来越重要。 GPU 是经常用于加速此类科学计算的计算设备，为了实现最大性能，软件必须针对所使用的特定 GPU 进行调整，尽管这种自动性能调整（自动调整）是常规的。在所有 GPU 都相同的传统超级计算环境中，该过程很有效，但在包含数百种不同类型 GPU 的环境中，这种异构性很常见，因为志愿者计算时间用于执行大量计算。通过开发能够处理异构性的自动调整算法、向程序员提供有关未实现性能的反馈以及在真实的志愿者计算系统中大规模工作，该项目将使这些系统能够最大限度地提高 GPU 性能并加速医学、生物学、和该项目的目标是高度自动地调整应用程序，以便以比在遇到的每个 GPU 上重新运行自动调整更低的成本在异构志愿者计算环境中获得最大性能。减少自动调整工作，同时使用跨不同 GPU 收集的性能数据来引导和加速自动调整过程。第二，性能模型与自动调整相集成，向程序员提供有关瓶颈和错过的性能优化机会的反馈。用于修剪应用程序性能景观的无效探索，所有这些都将在流行的现实世界 BOINC 志愿者计算系统中实施，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 学生开展推广活动。还通过提供高保真 GPU 模型来帮助保护电子游戏。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。