TCB Publications - Abstract

Heng Ma, Austin Clyde, Anda Trifan, Venkatram Vishwanath, Arvind Ramanathan, Debsindhu Bhowmik, and Shantenu Jha. Benchmarking machine learning workloads in structural bioinformatics applications. First International Workshop on Benchmarking Machine Learning Workloads on Emerging Hardware: CHALLENGE20, 2020.

MA2020-AT Benchmarking machine learning (ML) based methods have traditionally been largely uncoupled from scientific simulations. However, there has been considerable interest in using learning approaches in the context of scientific simulation software to: (1) analyze large volumes of simulation data (or archived databases);(2) drive adaptive simulations to sample ‘rare’ events; (3) accelerate simulations by replacing expensive computational kernels with efficient ML inference techniques, and (4) drive optimal simulation strategies based on ML guided approaches. Thus, the coupling of learning with simulation tools can range widely: from ML approaches which are independent of the application itself, to ML approaches which are used to drive large-scale simulations. Using structural bioinformatics applications, we motivate how ML approaches are coupled with physics-based simulations. To optimize such coupled applications on emerging hardware and software platforms, we need to consider additional and often unique performance considerations. In this paper, we present an overview of different learning approaches in structural bioinformatics applications, performance considerations for such coupled applications, and outline the development of performance metrics. We hope this will enable the broader scientific community as well as hardware and software vendors to evaluate the role of learning tools when coupled to scientific simulation applications, and hope that this could serve as a framework for other application domains.

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