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http://dx.doi.org/10.5762/KAIS.2014.15.7.4545

Analysis of Job Scheduling and the Efficiency for Multi-core Mobile GPU  

Lim, Hyojeong (Department of Computer Science and Engineering, Chungnam National University)
Han, Donggeon (Department of Computer Science and Engineering, Chungnam National University)
Kim, Hyungshin (Department of Computer Science and Engineering, Chungnam National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.15, no.7, 2014 , pp. 4545-4553 More about this Journal
Abstract
Mobile GPU has led to the rapid development of smart phone graphic technology. Most recent smart phones are equipped with high-performance multi-core GPU. How a multi-core mobile GPU can be utilized efficiently will be a critical issue for improving the smart phone performance. On the other hand, most current research has focused on a single-core mobile GPU; studies of multi-core mobile GPU are rare. In this paper, the job scheduling patterns and the efficiency of multi-core mobile GPU are analyzed. In the profiling result, despite the higher number of GPU cores, the total processing time required for certain graphics applications were increased. In addition, when GPU is processing for 3D games, a substantial amount of overhead is caused by communication between not only the CPU and GPU, but also within the GPUs. These results confirmed that more active research for multi-core mobile GPU should be performed to optimize the present mobile GPUs.
Keywords
Job scheduling; Mobile GPU; Multi-core GPU; Profiling;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 KATO, S., LAKSHMANAN, K., RAJKUMAR, R., ISHIKAWA, Y., "TimeGraph:GPU scheduling for real-time multi-tasking environments.", In Proc. of USENIX Annual Technical Conference, 2011.
2 M. Kim, W. Choi, "Range Query Method of R-tree for Efficient Parallel Processing on GPU", Journal of KIISE : Computing Practices and Letters, vol.18, no.5, 409-413, May. 2012.   과학기술학회마을
3 H. choi, H. Jeon, C. Kim, "Quantitative Analysys of the Negative Factors on the GPU Performance", Journal of KIISE : Computing Practices and Letters, vol.18, no.4, pp.257-350, Apr. 2012.
4 Khronos Group, "The OpenCL Specification", Version 1.0, 7, 2009.
5 Mian Dong, Lin Zhong, and Zhigang Deng, "Performance and Power Consumption Characterization of 3D Mobile Games", In IEEE Computer Society, 2011. DOI: http://dx.doi.org/10.1109/MC.2012.190   DOI   ScienceOn
6 GFXBench, GLBenchmark, http://www.glbenchmark.com/
7 NVIDIA, CUDA Toolkit, Available:https://developer.nvidia.com/cuda-downloads
8 TechPowerUp, GPU-Z, http://www.techpowerup.com/gpuz
9 Aurora Softworks, Quadrant Benchmark, http://www.aurorasoftworks.com/
10 Akenine-Moller, T. and Strom, J., "Graphics processing units for handhelds", Proceedings of the IEEE 96(5), 779-789, 2008 DOI: http://dx.doi.org/10.1109/JPROC.2008.917719   DOI   ScienceOn
11 ARM, DS-5 Streamline, www.arm.com
12 Sunpyo Hong, Hyesoon Kim, "An Integrated GPU Power and Performance Model.", In ACM SIGARCH Computer Architecture News, 2010. DOI: http://dx.doi.org/10.1145/1816038.1815998   DOI
13 NVIDIA, "NVIDIA CUDA Programming Guide", 1, 2.2, 7, 2011.
14 J.-H. Kim, J.-S. Kim "Implementation of Efficient Power Method on CUDA GPU." Journal of The Korea Society of Computer and Information, Vol. 16, No. 2, pp. 9-16, February 2011. DOI: http://dx.doi.org/10.9708/jksci.2011.16.2.009   과학기술학회마을   DOI
15 KATO, S., LAKSHMANAN, K., KUMAR, A., KELKAR, M., ISHIKAWA, Y., AND RAJKUMAR, R., "RGEM: A responsive GPGPU execution model for runtime engines.", In Proc. of IEEE Real-Time Systems Symposium, pp. 57-66, 2011. DOI: http://dx.doi.org/10.1109/RTSS.2011.13   DOI