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GTC Silicon Valley-2019 ID:S9594:Bringing State-of-the-Art GPU-Accelerated Molecular Modeling Tools to the Research Community

John Stone(University of Illinois at UrbanaChampaign)
We'll showcase the latest successes with GPU acceleration of challenging molecular simulation analysis tasks on the latest Volta and Turing GPUs paired with both Intel and IBM/OpenPOWER CPUs on petascale computers such as ORNL Summit. This presentation will highlight the performance benefits obtained from die-stacked memory, NVLink interconnects, and the use of advanced features of CUDA such as just-in-time compilation to increase the performance of key analysis algorithms. We will present results obtained with OpenACC parallel programming directives, as well as discuss current challenges and future opportunities. We'll also describe GPU-Accelerated machine learning algorithms for tasks such as clustering of structures resulting from molecular dynamics simulations. To make our tools easy to deploy for non-tradtional users of HPC, we publish GPU-Accelerated container images in NGC, and Amazon EC2 AMIs for GPU instance types.

View the slides (pdf)