- 综合性科技类中文核心期刊
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- 中国科学引文数据库来源期刊
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| Citation: |
FANG Juan, ZHANG Jiaxing. Task Scheduling Strategy of CPU-GPU Heterogeneous Computing Platform Based on Load Balancing[J]. Journal of Beijing University of Technology, 2020, 46(7): 782-787. DOI: 10.11936/bjutxb2019090015
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In central processing unit-graphics processing unit (CPU-GPU) heterogeneous system, the uneven performance of the CPU and GPU caused the system performance to decrease. A hybrid scheduling strategy based on queues was proposed to solve the problem. The computing power of the CPU and GPU was detected to process specified tasks, and computing tasks were allocated to the CPU and GPU according to the perception ratio. The tasks were stored in a bidirectional queue to reduce the additional overhead brought by scheduling. Results show that the system performance of the benchmark test program is improved by using this strategy by an average of 28.07%. Overall, the scheduling strategy can reduce the waiting time after the CPU and GPU complete their respective computing tasks, balance the load between the system CPU and GPU, and improve the system performance.
| [1] |
CHAU R. Process and packaging innovations for moore's law continuation and beyond[C]//IEEE International Electron Devices Meeting. Piscataway: IEEE, 2019: 111-116.
|
| [2] |
DANOWITZ A, KELLEY K, MAO J, et al. CPU DB:recording microprocessor history[J]. Communications of the ACM, 2012, 55(4):55-63. doi: 10.1145/2133806.2133822
|
| [3] |
SHEN J, VARBANESCU A L, ZOU P, et al. Improving performance by matching imbalanced workloads with heterogeneous platforms[C]//International Conference on Supercomputing. New York: Association for Computing Machinery, 2014: 241-250.
|
| [4] |
FANG J, LENG Z Y, LIU S T, et al. Exploring heterogeneous NoC design space in heterogeneous GPU-CPU architectures[J]. Journal of Computer Science and Technology, 2015, 30(1):74-83. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjkxjsxb-e201501007
|
| [5] |
方娟, 张希蓓, 刘士建.基于异构多核的LLC缓冲管理策略[J].北京工业大学学报, 2019, 45(5):421-427. doi: 10.11936/bjutxb2017090031
FANG J, ZHANG X B, LIU S J. LLC buffer management strategy based on heterogeneous multi-core[J]. Journal of Beijing University of Technology, 2019, 45(5):421-427. (in Chinese) doi: 10.11936/bjutxb2017090031
|
| [6] |
ITURRIAGA S, NESMACHNOW S, LUNA F, et al. A parallel local search in CPU/GPU for scheduling independent tasks on large heterogeneous computing systems[J]. The Journal of Supercomputing, 2015, 71(2):648-672. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=c57a9e81200328c9b2254af126bfb0e9
|
| [7] |
王彦华, 乔建忠, 林树宽, 等.基于SVM的CPU-GPU异构系统任务分配模型[J].东北大学学报(自然科学版), 2016, 37(8):1089-1094. doi: 10.3969/j.issn.1005-3026.2016.08.006
WANG Y H, QIAO J Z, LIN S K, et al. A task allocation model for CPU-GPU heterogeneous system based on SVMs[J]. Journal of Northeastern University (Natural Science), 2016, 37(8):1089-1094. (in Chinese) doi: 10.3969/j.issn.1005-3026.2016.08.006
|
| [8] |
ALSUBAIHI S, GAUDIOT J L. A runtime workload distribution with resource allocation for CPU-GPU hererogeneous system[C]//Parallel & Distributed Processing Symposium Workshops. Piscataway: IEEE, 2017: 994-1003.
|
| [9] |
SHULGA D A, KAPUSTIN A A, KOZLOV A A, et al. The scheduling based on machine learning for heterogeneous CPU/GPU systems[C]//IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference. Piscataway: IEEE, 2016: 345-348.
|
| [10] |
BAO Z, CHEN C, ZHANG W. Task scheduling of data-parallel applications on HSA platform[C]//International Conference of Pioneering Computer Scientists, Engineers and Educators. Berlin: Springer, 2018: 452-461.
|
| [11] |
MA K, LI X, CHEN W, et al. GreenGPU: a holistic approach to energy efficiency in GPU-CPU heterogeneous architectures[C]//International Conference on Parallel Processing. Piscataway: IEEE, 2012: 48-57.
|
| [12] |
SHEN W F, LUO Z K, WEI D M, et al. Load-prediction scheduling algorithm for computer simulation of electrocardiogram in hybrid environments[J]. Journal of Systems and Software, 2015(102):182-191. http://cn.bing.com/academic/profile?id=c9435ea810880fd9ef0849d283280b24&encoded=0&v=paper_preview&mkt=zh-cn
|
| [13] |
VILCHES A, ASENJO R, NAVARRO A, et al. Adaptive partitioning for irregular applications on heterogeneous CPU-GPU chips[J]. Procedia Computer Science, 2015(51):140-149. http://cn.bing.com/academic/profile?id=a0b7c8a2e8a97d80a67e8af183a74a2d&encoded=0&v=paper_preview&mkt=zh-cn
|
| [14] |
GARGI A, KAJAL V, SANTONU S. Predicting execution time of CUDA kernel using static analysis[C]//IEEE International Symposium on Parallel and Distributed Processing with Applications. Piscataway: IEEE, 2018: 948-955.
|
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