2776

Fast Analysis of Molecular Dynamics Trajectories with Graphics Processing Units – Radial Distribution Function Histogramming

Benjamin G. Levine, John E. Stone, Axel Kohlmeyer
Institute for Computational Molecular Science and Department of Chemistry, Temple University, Philadelphia, PA
Journal of Computational Physics (06 February 2011)

@article{Levine2011,

   title={“FastAnalysisofMolecularDynamicsTrajectorieswithGraphicsProcessingUnits–RadialDistributionFunctionHistogramming”},

   journal={“JournalofComputationalPhysics”},

   volume={“InPress},

   number={“”},

   pages={“-“},

   year={“2011”},

   note={“”},

   issn={“0021-9991”},

   doi={“DOI:10.1016/j.jcp.2011.01.048”},

   url={“http://www.sciencedirect.com/science/article/B6WHY-5241FGG-2/2/eede1ac15d5bc38c64ac8e0ae461286a”},

   author={“BenjaminG.LevineandJohnE.StoneandAxelKohlmeyer”},

   keywords={“GPGPU”}

}

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The calculation of radial distribution functions (RDFs) from molecular dynamics trajectory data is a common and computationally expensive analysis task. The rate limiting step in the calculation of the RDF is building a histogram of the distance between atom pairs in each trajectory frame. Here we present an implementation of this histogramming scheme for multiple graphics processing units (GPUs). The algorithm features a tiling scheme to maximize the reuse of data at the fastest levels of the GPU’s memory hierarchy and dynamic load balancing to allow high performance on heterogeneous configurations of GPUs. Several versions of the RDF algorithm are presented, utilizing the specific hardware features found on different generations of GPUs. We take advantage of larger shared memory and atomic memory operations available on state-of-the-art GPUs to accelerate the code significantly. The use of atomic memory operations allows the fast, limited-capacity on-chip memory to be used much more efficiently, resulting in a fivefold increase in performance compared to the version of the algorithm without atomic operations. The ultimate version of the algorithm running in parallel on four NVIDIA GeForce GTX 480 (Fermi) GPUs was found to be 92 times faster than a multithreaded implementation running on an Intel Xeon 5550 CPU. On this multi-GPU hardware, the RDF between two selections of 1,000,000 atoms each can be calculated in 26.9 seconds per frame. The multi-GPU RDF algorithms described here are implemented in VMD, a widely used and freely available software package for molecular dynamics visualization and analysis.
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