Collaborative Research: PPoSS: Planning: SEEr: A Scalable, Energy Efficient HPC Environment for AI-Enabled Science

合作研究：PPoSS：规划：SEEr：面向人工智能科学的可扩展、节能的 HPC 环境

• 批准号: 2119294
• 负责人: Zhiling Lan
• 金额: $ 15万
• 依托单位: Illinois Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119294&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; SEEr

AI-enabled science, where advanced machine-learning technologies are used for surrogate models, autotuning, and in situ data analysis, is quickly being adopted in science and engineering for tackling complex and challenging computational problems. The wide adoption of heterogeneous systems embedded with different types of processing devices (CPUs, GPUs, and AI accelerators) further complicates the execution of AI-enabled science on supercomputers. The research for AI-enabled simulations on heterogeneous systems is far from sufficient. The project’s novelty is to explore key features essential for a scalable, energy-efficient HPC environment for AI-enabled science on heterogeneous systems. The unified team of researchers tackles the problem in a cross-layer manner, focusing on the synergies among application algorithms, programming languages and compilers, runtime systems, and high-performance computing. The project's impact is to catalyze scientific discoveries by making scientific computing faster, more scalable and more energy-efficient. The long-term research vision is to develop SEEr, a scalable, energy-efficient HPC environment for scaling up and accelerating AI-enabled science for scientific discovery. This planning project explores fundamental questions to realize the research vision. The team focuses on scalable surrogate models for an incompressible computational fluid dynamics application using OpenFOAM, cost models for this application on heterogeneous resources, dynamic task mapping for efficient execution, and performance and power monitoring and characterization to explore tradeoffs among performance, scalability, and energy efficiency on a state-of-the-art testbed named Polaris.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.

人工智能支持的科学，其中先进的机器学习技术用于代理模型、自动调整和原位数据分析，正在科学和工程中迅速采用，以解决复杂且具有挑战性的计算问题，广泛采用嵌入不同的异构系统。不同类型的处理设备（CPU、GPU 和人工智能加速器）使超级计算机上人工智能科学的执行变得复杂。异构系统上人工智能仿真的研究还远远不够。该项目的新颖之处在于，探索异构系统上人工智能支持科学的可扩展、节能的 HPC 环境所必需的关键功能，统一的研究团队以跨层的方式解决该问题，重点关注应用算法之间的协同作用。该项目的影响是通过使科学计算更快、更可扩展和更节能来促进科学发现。开发 SEEr，这是一个可扩展、节能的 HPC 环境，用于扩大和加速人工智能支持的科学发现。该规划项目探讨了实现研究愿景的基本问题，该团队专注于不可压缩计算流体动力学的可扩展替代模型。使用 OpenFOAM 的应用程序、该应用程序在异构资源上的成本模型、用于执行的动态任务映射以及性能和功耗监控和表征，以探索性能、可扩展性和能源效率之间的权衡该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。