SEEr: A Scalable, Energy Efficient HPC Environment for AI-Enabled Science

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.

The ddiLab SEEr Team:

• Looking for students!
• Michael E. Papka

This work is in part funded by a National Science Foundation grant - SEEr: A Scalable, Energy Efficient HPC Environment for AI-Enabled Science.

This material is based upon work supported by the National Science Foundation under Grant No. CCF-2119056. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.