Accelerating Stochastic Simulations on GPUs Using OpenCL
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.11 pp.2253-2256
- Publication Date
- 2019/11/01
- Publicized
- 2019/07/23
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019EDL8030
- Type of Manuscript
- LETTER
- Category
- Fundamentals of Information Systems
Authors
Pilsung KANG
Sun Moon University
Keyword
Latest Issue
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Pilsung KANG, "Accelerating Stochastic Simulations on GPUs Using OpenCL" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 11, pp. 2253-2256, November 2019, doi: 10.1587/transinf.2019EDL8030.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8030/_p
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@ARTICLE{e102-d_11_2253,
author={Pilsung KANG, },
journal={IEICE TRANSACTIONS on Information},
title={Accelerating Stochastic Simulations on GPUs Using OpenCL},
year={2019},
volume={E102-D},
number={11},
pages={2253-2256},
abstract={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.},
keywords={},
doi={10.1587/transinf.2019EDL8030},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Accelerating Stochastic Simulations on GPUs Using OpenCL
T2 - IEICE TRANSACTIONS on Information
SP - 2253
EP - 2256
AU - Pilsung KANG
PY - 2019
DO - 10.1587/transinf.2019EDL8030
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2019
AB - 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.
ER -
English
