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Found 4 papers, 1 papers with code
Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs
Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines.
Exascale Deep Learning for Scientific Inverse Problems
We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grouping of gradient tensors.
Task Bench: A Parameterized Benchmark for Evaluating Parallel Runtime Performance
We present Task Bench, a parameterized benchmark designed to explore the performance of parallel and distributed programming systems under a variety of application scenarios.
Distributed, Parallel, and Cluster Computing
Exascale Deep Learning for Climate Analytics
The Tiramisu network scales to 5300 P100 GPUs with a sustained throughput of 21. 0 PF/s and parallel efficiency of 79. 0%.
Distributed, Parallel, and Cluster Computing
