30 citations · 49 across the 4 of their papers we have counts for
3 papers · 1 filter
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Liu Yang, Sean Treichler, Thorsten Kurth +8
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling…
Exascale Deep Learning for Scientific Inverse Problems
Nouamane Laanait, Joshua Romero, Junqi Yin +6
We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grou…
Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance
Elliott Slaughter, Wei Wu, Yuankun Fu +12
We present Task Bench, a parameterized benchmark designed to explore the performance of parallel and distributed programming systems under a variety of application scenarios. Task…