Article | Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 | Classification of OpenCL Kernels for accelerating Java Multi-agent Simulation Linköping University Electronic Press Conference Proceedings
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Title:
Classification of OpenCL Kernels for accelerating Java Multi-agent Simulation
Author:
Pitipat Penbharkkul: Department of Computer Science, Thammasat University, Pathum Thani, Thailand Worawan Marurngsith: Department of Computer Science, Thammasat University, Pathum Thani, Thailand
DOI:
10.3384/ecp17142805
Download:
Full text (pdf)
Year:
2018
Conference:
Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Issue:
142
Article no.:
118
Pages:
805-811
No. of pages:
7
Publication type:
Abstract and Fulltext
Published:
2018-12-19
ISBN:
978-91-7685-399-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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Java-based multi-agent simulation (MAS) can be offloaded to graphical processing units (GPU) and other OpenCL accelerators to achieve many hundred-fold speedups. However, the performance gain from the accelerated code depends strongly on whether the computation (kernels) have been scheduled to the appropriate devices. Thus, accelerating Java MAS may not lead to a sustainable speedup. This paper proposes a method for a kernel classifier to specify suitable devices to execute OpenCL kernels. The classifier can identify suitable OpenCL devices for kernels based on the static and dynamic characteristics of the code of the kernels. Kernels are grouped by their suitability for particular devices using the multiclass support virtual machine technique. After that, kernels are scheduled to an appropriate task queue. Kernel scheduling based on the proposed technique is compared against the first-come-first-serve (FCFS) technique and against oracle scheduling when handling eight kernels. Our results show that, using the proposed method, all kernels finished execution 45 percent sooner than using the FCFS technique. However, the overall execution time was 22.5 percent longer than with oracle scheduling. Our results seem to confirm that kernel classification techniques might contribute towards sustainable high performance in accelerated Java-based MAS models.

Keywords: GPGPU, OpenCL, multi-agent simulation, performance, acceleration, SVM, MASON

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Pitipat Penbharkkul, Worawan Marurngsith
Title:
Classification of OpenCL Kernels for accelerating Java Multi-agent Simulation
DOI:
http://dx.doi.org/10.3384/ecp17142805
References:
No references available

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Pitipat Penbharkkul, Worawan Marurngsith
Title:
Classification of OpenCL Kernels for accelerating Java Multi-agent Simulation
DOI:
https://doi.org10.3384/ecp17142805
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Last updated: 2019-10-02