How Machine Learning Is Revolutionizing HPC Simulations - High-Performance Computing News Analysis

Physics-based simulations, that staple of traditional HPC, may be evolving toward an emerging, AI-based technique that could radically accelerate simulation runs while cutting costs. Called "surrogate machine learning models," the topic was a focal point in a keynote on Tuesday at the International Conference on Parallel Processing by Argonne National Lab's Rick Stevens. Stevens, ANL's associate laboratory director for computing, environment and life sciences, said early work in "surrogates," as the technique is called, shows tens of thousands of times (and more) speed-ups and could "potentially replace simulations." In his keynote, entitled, "Exascale and Then What?: The Next Decade for HPC and AI," Stevens explained surrogates this way: "You have a system, it could be a molecular system or drug design…, and you have a physics-based simulation of it… You run this code and capture the input-output relationships of the core simulation… You use that training data to build an approximate model. These are typically done with neural networks… and this surrogate model approximates the simulation, and typically it is much faster. Of course, it has some errors, so then you use that surrogate model to search the space, or to advance time steps. And then maybe you do a correction step later."