7 citations · 10 across the 3 of their papers we have counts for
3 papers
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
Benchmarking GPU and TPU Performance with Graph Neural Networks
xiangyang Ju, Yunsong Wang, Daniel Murnane +3
Many artificial intelligence (AI) devices have been developed to accelerate the training and inference of neural networks models. The most common ones are the Graphics Processing U…
Portability: A Necessary Approach for Future Scientific Software
Meghna Bhattacharya, Paolo Calafiura, Taylor Childers +16
Today's world of scientific software for High Energy Physics (HEP) is powered by x86 code, while the future will be much more reliant on accelerators like GPUs and FPGAs. The porta…