30 citations · 37 across the 8 of their papers we have counts for
5 papers · 1 filter
Stable Anderson Acceleration for Deep Learning
Massimiliano Lupo Pasini, Junqi Yin, Viktor Reshniak +1
Anderson acceleration (AA) is an extrapolation technique designed to speed-up fixed-point iterations like those arising from the iterative training of DL models. Training DL models…
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Steven Farrell, Murali Emani, Jacob Balma +40
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing syste…
Data optimization for large batch distributed training of deep neural networks
Shubhankar Gahlot, Junqi Yin, Mallikarjun Shankar
Distributed training in deep learning (DL) is common practice as data and models grow. The current practice for distributed training of deep neural networks faces the challenges of…
Integrating Deep Learning in Domain Sciences at Exascale
Rick Archibald, Edmond Chow, Eduardo D'Azevedo +8
This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simu…
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…