Journal
2020 28TH EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND NETWORK-BASED PROCESSING (PDP 2020)
Volume -, Issue -, Pages 162-166Publisher
IEEE COMPUTER SOC
DOI: 10.1109/PDP50117.2020.00031
Keywords
Energy efficiency; power profiling; OpenCL; molecular docking; AutoDock; gradients
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Funding
- German Academic Exchange Service (DAAD)
- Peruvian National Program
- AMD Inc.
- AWS Credits for Research program
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AuToDocK is a molecular docking application that consists of a genetic algorithm coupled with the Solis-Wets local-search method. Despite its wide usage, its power consumption on heterogeneous systems has not been evaluated extensively. In this work, we evaluate the energy efficiency of an OpenCL-accelerated version of AuToDocK that, along with the traditional Solis-Wets method, newly incorporates the ADADELTA gradient-based local search. Executions on a Nvidia V100 GPU yielded energy efficiency improvements of up to 297x (Solis-Wets) and 137x (ADADELTA) with respect to the original AuToDocK baseline.
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