Efficient Task Distribution for Pig Monitoring Applications Using OpenCL

KIPS Transactions on Computer and Communication Systems, Vol. 6, No.10, pp.407-414, October 2017
10.3745/KTCCS.2017.6.10.407, Full Text

Abstract

Pig monitoring applications consisting of many tasks can take advantage of inherent data parallelism and enable parallel processing using performance accelerators. In this paper, we propose a task distribution method for pig monitoring applications into a heterogenous computing platform consisting of a multicore-CPU and a manycore-GPU. That is, a parallel program written in OpenCL is developed, and then the most suitable processor is determined based on the measured execution time of each task. The proposed method is simple but very effective, and can be applied to parallelize other applications consisting of many tasks on a heterogeneous computing platform consisting of a CPU and a GPU. Experimental results show that the performance of the proposed task distribution method on three different heterogeneous computing platforms can improve the performance of the typical GPU-only method where every tasks are executed on a deviceGPU by a factor of 1.5, 8.7 and 2.7, respectively.


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Cite this paper

[KIPS Transactions Style]
J. Kim, Y. Choi, J. Kim, Y. Chung, Y. Chung, D. Park, and H. Kim, "Efficient Task Distribution for Pig Monitoring Applications Using OpenCL," KIPS Transactions on Computer and Communication Systems, Vol.6, No.10, pp.407-414, 2017, DOI: 10.3745/KTCCS.2017.6.10.407.

[IEEE Style]
Jinseong Kim, Younchang Choi, Jaehak Kim, Yeonwoo Chung, Yongwha Chung, Daihee Park, and Hakjae Kim, "Efficient Task Distribution for Pig Monitoring Applications Using OpenCL," KIPS Transactions on Computer and Communication Systems, vol. 6, no. 10, pp. 407-414, 2017. DOI: 10.3745/KTCCS.2017.6.10.407.

[ACM Style]
Kim, J., Choi, Y., Kim, J., Chung, Y., Chung, Y., Park, D., and Kim, H. 2017. Efficient Task Distribution for Pig Monitoring Applications Using OpenCL. KIPS Transactions on Computer and Communication Systems, 6, 10, (2017), 407-414. DOI: 10.3745/KTCCS.2017.6.10.407.