TY - JOUR
T1 - Multilevel parallelization of autodock 4.2
AU - Norgan, Andrew P.
AU - Coffman, Paul K.
AU - Kocher, Jean Pierre A.
AU - Katzmann, David J.
AU - Sosa, Carlos P.
N1 - Funding Information:
We thank Cindy Mestad and Steven Westerbeck at IBM Rochester, David Singer and Fred Mintzer at IBM Watson and Sharon Selzo at IBM Poughkeepsie for technical assistance, and IBM corporation for providing access to the Blue Gene/P and POWER7 systems used in this study. We acknowledge the Minnesota Supercomputing Institute for providing technical support and computational resources for this study. We are grateful to Michael Pique for thoughtful discussions and reviewing this manuscript. This work was supported by an American Heart Association Predoctoral Fellowship 09PRE2220147 (APN), NIH Predoctoral Fellowship F30DA26762 (APN), and University of Minnesota-Rochester, Bioinformatics and Computational Biology (BICB) Program Seed Grant (DJK, CPS, JPK). The distribution of the mpAD4 software is supported by NIH Grant R01 GM069832 (A. Olson, The Scripps Research Institute).
PY - 2011
Y1 - 2011
N2 - Background: Virtual (computational) screening is an increasingly important tool for drug discovery. AutoDock is a popular open-source application for performing molecular docking, the prediction of ligand-receptor interactions. AutoDock is a serial application, though several previous efforts have parallelized various aspects of the program. In this paper, we report on a multi-level parallelization of AutoDock 4.2 (mpAD4). Results: Using MPI and OpenMP, AutoDock 4.2 was parallelized for use on MPI-enabled systems and to multithread the execution of individual docking jobs. In addition, code was implemented to reduce input/output (I/O) traffic by reusing grid maps at each node from docking to docking. Performance of mpAD4 was examined on two multiprocessor computers. Conclusions: Using MPI with OpenMP multithreading, mpAD4 scales with near linearity on the multiprocessor systems tested. In situations where I/O is limiting, reuse of grid maps reduces both system I/O and overall screening time. Multithreading of AutoDock's Lamarkian Genetic Algorithm with OpenMP increases the speed of execution of individual docking jobs, and when combined with MPI parallelization can significantly reduce the execution time of virtual screens. This work is significant in that mpAD4 speeds the execution of certain molecular docking workloads and allows the user to optimize the degree of system-level (MPI) and node-level (OpenMP) parallelization to best fit both workloads and computational resources.
AB - Background: Virtual (computational) screening is an increasingly important tool for drug discovery. AutoDock is a popular open-source application for performing molecular docking, the prediction of ligand-receptor interactions. AutoDock is a serial application, though several previous efforts have parallelized various aspects of the program. In this paper, we report on a multi-level parallelization of AutoDock 4.2 (mpAD4). Results: Using MPI and OpenMP, AutoDock 4.2 was parallelized for use on MPI-enabled systems and to multithread the execution of individual docking jobs. In addition, code was implemented to reduce input/output (I/O) traffic by reusing grid maps at each node from docking to docking. Performance of mpAD4 was examined on two multiprocessor computers. Conclusions: Using MPI with OpenMP multithreading, mpAD4 scales with near linearity on the multiprocessor systems tested. In situations where I/O is limiting, reuse of grid maps reduces both system I/O and overall screening time. Multithreading of AutoDock's Lamarkian Genetic Algorithm with OpenMP increases the speed of execution of individual docking jobs, and when combined with MPI parallelization can significantly reduce the execution time of virtual screens. This work is significant in that mpAD4 speeds the execution of certain molecular docking workloads and allows the user to optimize the degree of system-level (MPI) and node-level (OpenMP) parallelization to best fit both workloads and computational resources.
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U2 - 10.1186/1758-2946-3-12
DO - 10.1186/1758-2946-3-12
M3 - Article
C2 - 21527034
AN - SCOPUS:79957522491
SN - 1758-2946
VL - 3
JO - Journal of Cheminformatics
JF - Journal of Cheminformatics
IS - 1
M1 - 12
ER -