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

基本信息

批准号：
2119203
负责人：
Valerie Taylor
金额：
$ 6万
依托单位：
University of Chicago
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 2022-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2119203&HistoricalAwards=false
关键词：
Collaborative Research PPoSS Planning SEEr

项目摘要

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.

AI支持的科学，即先进的机器学习技术用于替代模型，自动调整和原位数据分析，在科学和工程中迅速采用了解决复杂和挑战计算问题的科学。嵌入不同类型的处理设备（CPU，GPU和AI加速器）嵌入的异质系统的广泛采用使对超级计算机的AI支持科学的执行更加复杂。对异质系统的启用AI启用模拟的研究远远不够。该项目的新颖性是探索对于在异质系统上为AI支持的科学的可扩展，节能的HPC环境所必需的关键功能。统一的研究人员团队以跨层的方式解决了该问题，重点是应用算法，编程语言和编译器，运行时系统和高性能计算之间的协同作用。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催化科学发现。长期的研究愿景是开发SEER，这是一种可扩展的，节能的HPC环境，用于扩展和加速AI-ai-ai-ai-abable科学科学，以进行科学发现。该计划项目探讨了基本问题以实现研究愿景。该团队专注于使用OpenFOAM的不可压缩计算流体动力学应用，该应用程序上的成本模型，在异质资源上的成本模型，有效执行的动态任务映射以及绩效和功率监控，以探索绩效，可扩展性和能源效率之间的权衡，并使用正式的测试台上的降级和降级。基金会的智力优点和更广泛的影响审查标准。