Achieving 100X faster simulations of complex biological phenomena by coupling ML to HPC ensembles

• URL: http://arxiv.org/abs/2104.04797v1
• Date: Sat, 10 Apr 2021 15:52:39 GMT
• Title: Achieving 100X faster simulations of complex biological phenomena by coupling ML to HPC ensembles
• Authors: Alexander Brace, Hyungro Lee, Heng Ma, Anda Trifan, Matteo Turilli, Igor Yaskushin, Todd Munson, Ian Foster, Shantenu Jha and Arvind Ramanathan
• Abstract summary: We present DeepDriveMD, a tool for a range of prototypical ML-driven HPC simulation scenarios. We use it to quantify improvements in the scientific performance of ML-driven ensemble-based applications.
• Score: 47.44377051031385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The use of ML methods to dynamically steer ensemble-based simulations promises significant improvements in the performance of scientific applications. We present DeepDriveMD, a tool for a range of prototypical ML-driven HPC simulation scenarios, and use it to quantify improvements in the scientific performance of ML-driven ensemble-based applications. We discuss its design and characterize its performance. Motivated by the potential for further scientific improvements and applicability to more sophisticated physical systems, we extend the design of DeepDriveMD to support stream-based communication between simulations and learning methods. It demonstrates a 100x speedup to fold proteins, and performs 1.6x more simulations per unit time, improving resource utilization compared to the sequential framework. Experiments are performed on leadership-class platforms, at scales of up to O(1000) nodes, and for production workloads. We establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse simulation and ML back-ends, and which enables new scientific insights by improving length- and time-scale accessed.

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