Abstract

Scientific models are today limited by compute resources, forcing approximations driven by feasibility rather than theory. They consequently miss important physical processes and decision-relevant regional details. Advances in AI-driven supercomputing — specialized tensor accelerators, AI compiler stacks, and novel distributed systems — offer unprecedented computational power. Yet, scientific applications such as ocean models, often written in Fortran, C++, or Julia and built for traditional HPC, remain largely incompatible with these technologies. This gap hampers performance portability and isolates scientific computing from rapid cloud-based innovation for AI workloads. In this talk, we bridge that gap by transpiling existing programs using the MLIR compiler infrastructure. This process enables advanced optimizations, deployment on AI hardware, and automatic differentiation. In particular, we demonstrate execution of a state of the art Julia-based ocean model (Oceananigans), with >277 custom single-node CUDA kernels on thousands of distributed GPUs and Google TPUs. Our results demonstrate that cloud-based hardware and software designed for AI workloads can significantly accelerate simulations, opening a path for scientific programs to benefit from cutting-edge computational advances.

Bio William Moses is an Assistant Professor at the University of Illinois in the Computer Science and Electrical and Computer Engineering departments. He received a Ph.D. in Computer Science from MIT, where he also received his M.Eng in electrical engineering and computer science (EECS) and B.S. in EECS and physics. William's research involves creating compilers and program representations that enable performance and use-case portability, thus enabling non-experts to leverage the latest in high-performance computing and ML. He is known as the lead developer of Enzyme, a tool for LLVM/MLIR capable of differentiating code in a variety of languages; Polygeist, a polyhedral compiler and C++ frontend for MLIR; and Reactant, a tool for enabling existing scientific code to run on distributed ML accelerators. He has also worked on the Tensor Comprehensions framework for synthesizing high-performance GPU kernels of ML code, the Tapir compiler for parallel programs, and compilers that use machine learning to better optimize. He is a recipient of the SIAM SC Early Career Prize, the SIGHPC Doctoral Dissertation Award, a DOE Computational Science Graduate Fellowship and the Karl Taylor Compton Prize, MIT's highest student award.