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An Efficient Deterministic Parallel Algorithm for Adaptive Multidimensional Numerical Integration on GPUs

Kamesh Arumugam, Alexander Godunov, Desh Ranjan, Balsa Terzic, Mohammad Zubair
Department of Computer Science, Old Dominion University, Norfolk, Virginia 23529
The 42nd Annual Conference International Conference on Parallel Processing, 2013

@article{arumugam2013efficient,

   title={An Efficient Deterministic Parallel Algorithm for Adaptive Multidimensional Numerical Integration on GPUs},

   author={Arumugam, Kamesh and Godunov, Alexander and Ranjan, Desh and Terzic, Bal{v{s}}a and Zubair, Mohammad},

   year={2013}

}

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Recent development in Graphics Processing Units (GPUs) has enabled a new possibility for highly efficient parallel computing in science and engineering. Their massively parallel architecture makes GPUs very effective for algorithms where processing of large blocks of data can be executed in parallel. Multidimensional integration has important applications in areas like computational physics, plasma physics, computational fluid dynamics, quantum chemistry, molecular dynamics and signal processing. The computationally intensive nature of multidimensional integration requires a high-performance implementation. In this study, we present an efficient deterministic parallel algorithm for adaptive multidimensional numerical integration on GPUs. Various optimization techniques are applied to maximize the utilization of the GPU. GPU-based implementation outperforms the best known sequential methods and achieves a speed-up of up to 100. It also shows good scalability with the increase in dimensionality.
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