Simulating Molecular Docking on the SX-Aurora TSUBASA Vector Engine

Leonardo Solis-Vasquez (), Erich Focht () and Andreas Koch ()
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Leonardo Solis-Vasquez: Technical University of Darmstadt
Erich Focht: NEC Deutschland GmbH
Andreas Koch: Technical University of Darmstadt

A chapter in Sustained Simulation Performance 2021, 2023, pp 21-35 from Springer

Abstract: Abstract Molecular docking simulations are widely used in computational drug discovery. These simulations aim to predict molecular interactions at close distances by executing compute-intensive calculations. In recent years, the usage of hardware accelerators to speedup such simulations has become essential, since by leveraging their processing capabilities, the time-consuming identification of potential drug candidates can be significantly shortened. AutoDock is one of the most cited software applications for molecular docking simulations. In this work, we present our experiences in porting and optimizing an OpenCL-based AutoDock implementation on the SX-Aurora TSUBASA Vector Engine. For this purpose, we use device-specific coding techniques in order to leverage the multiple cores on the Vector Engine, as well as its internal vector-based processing capabilities. Based on our experiments,we achieve 3.6× speedup by using a SX-Aurora TSUBASAVE20B model compared to modern multi-core CPUs, while still achieving competitive performance with respect to modern high-end GPUs.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-18046-0_2

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DOI: 10.1007/978-3-031-18046-0_2

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