Presentation
Seamlessly scale your python program from single CPU core to multi-GPU multi-node HPC cluster with cuNumeric
DescriptionPython is a powerful and user-friendly programming language widely adopted by researchers and scientists. As data sizes and computational complexities grow, CPU-based Python struggles to meet the speed and scale demanded by cutting-edge research. Distributed accelerated computing offers an infrastructure to efficiently solve and test hypotheses in data-driven problems. Whether it’s analyzing data generated by recording the scattering of high-energy electron beams, building new methodology to solve complex CFD problems, or build machine learning (ML) models. Researchers are increasingly seeking ways to effortlessly scale their programs. Our upcoming demonstration will provide a comprehensive walkthrough on how to use cuNumeric and Legate to seamlessly scale your Python programs from a single CPU core to multi-GPU, multi-node supercomputers without any modifications to your code.
Acknowledgment and potential co-presenter:
Jason R. Green, Professor, Department of Chemistry, Department of Physics, University of Massachusetts, Boston
Pat McCormick, Senior Computer Scientist, Team Leader, LANL
Acknowledgment and potential co-presenter:
Jason R. Green, Professor, Department of Chemistry, Department of Physics, University of Massachusetts, Boston
Pat McCormick, Senior Computer Scientist, Team Leader, LANL
