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

人工智能支持的科学，将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，正在迅速应用于科学和工程领域，以解决复杂和具有挑战性的计算问题。广泛采用嵌入不同类型处理设备（cpu、gpu和人工智能加速器）的异构系统，进一步使人工智能科学在超级计算机上的执行复杂化。对人工智能在异构系统上的仿真研究还远远不够。该项目的新颖之处在于探索在异构系统上为人工智能科学提供可扩展、节能的高性能计算环境所必需的关键特性。统一的研究团队以跨层方式解决问题，重点关注应用程序算法、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。长期研究愿景是开发SEEr，这是一种可扩展的、节能的高性能计算环境，用于扩大和加速科学发现的人工智能科学。本规划项目探索基本问题，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程序的可扩展代理模型，该应用程序在异构资源上的成本模型，高效执行的动态任务映射，以及性能和功率监控和表征，以探索在最先进的测试平台Polaris上的性能，可扩展性和能效之间的权衡。该奖项反映了美国国家科学基金会的法定使命，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。

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.