A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators

no code implementations • 6 Oct 2023 • Murali Emani, Sam Foreman, Varuni Sastry, Zhen Xie, Siddhisanket Raskar, William Arnold, Rajeev Thakur, Venkatram Vishwanath, Michael E. Papka

However, the comparative performance of these AI accelerators on large language models has not been previously studied.

Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources

2 code implementations • 15 Mar 2023 • Logan Ward, J. Gregory Pauloski, Valerie Hayot-Sasson, Ryan Chard, Yadu Babuji, Ganesh Sivaraman, Sutanay Choudhury, Kyle Chard, Rajeev Thakur, Ian Foster

Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supercomputers and AI training and inference tasks on specialized accelerators.

Management

Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing

1 code implementation • 6 Oct 2021 • Logan Ward, Ganesh Sivaraman, J. Gregory Pauloski, Yadu Babuji, Ryan Chard, Naveen Dandu, Paul C. Redfern, Rajeev S. Assary, Kyle Chard, Larry A. Curtiss, Rajeev Thakur, Ian Foster

Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform.

BIG-bench Machine Learning