19193

Accelerating Stochastic Simulations on GPUs Using OpenCL

Pilsung Kang
Division of Computer Science and Engineering, Sun Moon University, South Korea
IEICE Transactions on Information and Systems, v.E102.D, issue 11, p. 2253-2256, 2019

@article{kang2019accelerating,

   title={Accelerating Stochastic Simulations on GPUs Using OpenCL},

   author={KANG, Pilsung},

   journal={IEICE Transactions on Information and Systems},

   volume={102},

   number={11},

   pages={2253–2256},

   year={2019},

   publisher={The Institute of Electronics, Information and Communication Engineers}

}

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Since first introduced in 2008 with the 1.0 specification, OpenCL has steadily evolved over the decade to increase its support for heterogeneous parallel systems. In this paper, we accelerate stochastic simulation of biochemical reaction networks on modern GPUs (graphics processing units) by means of the OpenCL programming language. In implementing the OpenCL version of the stochastic simulation algorithm, we carefully apply its data-parallel execution model to optimize the performance provided by the underlying hardware parallelism of the modern GPUs. To evaluate our OpenCL implementation of the stochastic simulation algorithm, we perform a comparative analysis in terms of the performance using the CPU-based cluster implementation and the NVidia CUDA implementation. In addition to the initial report on the performance of OpenCL on GPUs, we also discuss applicability and programmability of OpenCL in the context of GPU-based scientific computing.
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