18 citations · 23 across the 5 of their papers we have counts for
6 papers
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…
Scientific Machine Learning Benchmarks
Jeyan Thiyagalingam, Mallikarjun Shankar, Geoffrey Fox +1
The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. These datase…
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…
DataFed: Towards Reproducible Research via Federated Data Management
Dale Stansberry, Suhas Somnath, Jessica Breet +2
The increasingly collaborative, globalized nature of scientific research combined with the need to share data and the explosion in data volumes present an urgent need for a scienti…
USID and Pycroscopy -- Open frameworks for storing and analyzing spectroscopic and imaging data
Suhas Somnath, Chris R. Smith, Nouamane Laanait +7
Materials science is undergoing profound changes due to advances in characterization instrumentation that have resulted in an explosion of data in terms of volume, velocity, variet…
Defining Big Data Analytics Benchmarks for Next Generation Supercomputers
Drew Schmidt, Junqi Yin, Michael Matheson +2
The design and construction of high performance computing (HPC) systems relies on exhaustive performance analysis and benchmarking. Traditionally this activity has been geared excl…