MIT professor and CSAIL principal investigator Charles Leiserson and MIT CSAIL PhD student William Moses are among a team of researchers recently awarded funding by The Department of Energy for their high-performance computing project under the 2022 “Exploratory Research for Extreme-Scale Science” (EXPRESS) program.
Led by Lawrence Livermore National Laboratory researchers, the team was one of 22 to receive the prize at the EXPRESS Awards on September 19. LLNL mathematician Tzanio Kolev will serve as principal investigator on the project, with fellow LLNL researchers Julian Andrej, Boyan Lazarov, and Harshitha Menon contributing, in addition to Leiserson and co-investigator Moses.
The team’s project will address the challenge of efficiently differentiating large-scale DOE applications, predicting how adjustments in design parameters will impact the output of a code. Knowledge of optimal outputs is increasingly needed for complex simulation codes to be used for design optimization, machine learning, uncertainty quantification, and sensitivity analysis, among other applications. While automatic differentiation (AD) has made the differentiation process easier, “traditional AD tools require significant changes that are not feasible for many existing large-scale DOE applications,” says Kolev.
The team’s approach, which will focus on using finite element simulations and recent developments in AD, will be “different from ‘black box’ approaches,” according to Kolev. The researchers plan to build on advances in the MFEM finite element library, developed at LLNL to provide a common discretization layer for HPC applications, and MIT’s Enzyme AD tool, which can perform AD alongside compiler optimization without the need for code changes.
“The goal of this project is to enable, for the first time, fast and easy-to-use computation of operator derivatives and gradients of functionals in general finite element DOE applications,” Kolev said. “Combining MFEM and Enzyme will allow us to develop algorithms that are faster, require less memory, and are more robust,” says Kolev.
The EXPRESS projects will address emerging technologies such as high-end computing, massive datasets, artificial intelligence, and heterogeneous architectures including neuromorphic and quantum computing systems, according to DOE. The funding opportunity is sponsored by the Office of Advanced Scientific Computing Research within DOE’s Office of Science and is intended to explore high-impact, disruptive approaches to accelerate discoveries in scientific computing and extreme-scale science.
