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Improving numerical reproducibility and stability in large-scale numerical simulations on GPUs

Michela Taufer, Omar Padron, Philip Saponaro, Sandeep Patel
Dept. of Computer & Inf. Sciences, University of Delaware, Newark, DE, USA
IEEE International Symposium on Parallel & Distributed Processing (IPDPS), 2010

@conference{taufer2010improving,

   title={Improving numerical reproducibility and stability in large-scale numerical simulations on GPUs},

   author={Taufer, M. and Padron, O. and Saponaro, P. and Patel, S.},

   booktitle={Parallel & Distributed Processing (IPDPS), 2010 IEEE International Symposium on},

   pages={1–9},

   issn={1530-2075},

   organization={IEEE}

}

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The advent of general purpose graphics processing units (GPGPU’s) brings about a whole new platform for running numerically intensive applications at high speeds. Their multi-core architectures enable large degrees of parallelism via a massively multi-threaded environment. Molecular dynamics (MD) simulations are particularly well-suited for GPU’s because their computations are easily parallelizable. Significant performance improvements are observed when single precision floating-point arithmetic is used. However, this performance comes at the cost of accuracy: it is widely acknowledged that constant-energy (NVE) MD simulations accumulate errors as the simulation proceeds due to the inherent errors associated with integrators used for propagating the coordinates. A consequence of this numerical integration is the drift of potential energy as the simulation proceeds. Double precision arithmetic partially corrects this drifting, but is significantly slower than single precision, comparable to CPU performance. To address this problem, we extend the approaches of previous literature to improve numerical reproducibility and stability in MD simulations, while assuring efficiency and performance comparable to that when using the GPU hardware implementation of single precision arithmetic. We present development of a library of mathematical functions that use fast and efficient algorithms to fix the error produced by the equivalent operations performed by GPU. We successfully validate the library with a suite of synthetic codes emulating the MD behavior on GPUs.
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