1 |
吕相文,袁家斌,张玉洁.云计算环境下多GPU资源调度机制研究[J].小型微型计算机系统,2016,37(4):687-693. 10.3969/j.issn.1000-1220.2016.04.009
|
|
LYU X W, YUAN J B, ZHANG Y J. Study on resource scheduling mechanism of multi-GPU in cloud computing environment [J]. Journal of Chinese Computer Systems, 2016, 37(4): 687-693. 10.3969/j.issn.1000-1220.2016.04.009
|
2 |
PAI S, THAZHUTHAVEETIL M J, GOVINDARAJAN R. Improving GPGPU concurrency with elastic kernels [C]// Proceedings of the 2013 18th International Conference on Architectural Support for Programming Languages and Operating Systems. New York: ACM, 2013: 407-418. 10.1145/2451116.2451160
|
3 |
NVIDIA Corporation. Tuning CUDA applications for Kepler [EB/OL]. [2021-08-02]. .
|
4 |
SUZUKI Y, KATO S, YAMADA H, et al. GPUvm: why not virtualizing GPUs at the hypervisor? [C]// Proceedings of the 2014 USENIX Technical Conference. Berkeley: USENIX Association, 2014: 109-120.
|
5 |
KRIEDER S J, WOZNIAK J M, ARMSTRONG T, et al. Design and evaluation of the GeMTC framework for GPU-enabled many-task computing [C]// Proceedings of the 2014 23rd International Symposium on High-performance Parallel and Distributed Computing. New York: ACM, 2014: 153-164. 10.1145/2600212.2600228
|
6 |
YEH T T, SABNE A, SAKDHNAGOOL P, et al. Pagoda: fine-grained GPU resource virtualization for narrow tasks [C]// Proceedings of the 2017 22nd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. New York: ACM, 2017: 221-234. 10.1145/3018743.3018754
|
7 |
SHAO J L, MA J M, LI Y, et al. GPU scheduling for short tasks in private cloud [C]// Proceedings of the 2019 IEEE International Conference on Service-Oriented System Engineering. Piscataway: IEEE, 2019: 215-220. 10.1109/sose.2019.00037
|
8 |
FUNG W W L, SHAM I, YUAN G, et al. Dynamic warp formation and scheduling for efficient GPU control flow [C]// Proceedings of the 2007 40th Annual IEEE/ACM International Symposium on Microarchitecture. Piscataway: IEEE, 2007: 407-420. 10.1109/micro.2007.30
|
9 |
李涛,董前琨,张帅,等.基于线程池的GPU任务并行计算模式研究[J].计算机学报,2018,41(10):2175-2192. 10.11897/SP.J.1016.2018.02175
|
|
LI T, DONG Q K, ZHANG S, et al. GPU task parallel computing paradigm based on thread pool model [J]. Chinese Journal of Computers, 2018, 41(10): 2175-2192. 10.11897/SP.J.1016.2018.02175
|
10 |
ZHU Z, LI J J, LI G H. Load-balanced breadth-first search on GPUs [C]// Proceedings of the 2014 International Conference on Web-Age Information Management, LNCS8485. Cham: Springer, 2014: 435-447.
|
11 |
LI J, LIU L, WU Y, et al. Two-level task scheduling for irregular applications on GPU platform [J]. International Journal of Parallel Programming, 2017, 45(1): 79-93. 10.1007/s10766-015-0387-0
|
12 |
FANG J, ZHANG J X, LU S B, et al. Exploration on task scheduling strategy for CPU-GPU heterogeneous computing system [C]// Proceedings of the 2020 IEEE Computer Society Annual Symposium on VLSI. Piscataway: IEEE, 2020: 306-311. 10.1109/isvlsi49217.2020.00063
|
13 |
SINGH H, SINGH G. Task scheduling in cluster computing environment [C]// Proceedings of the 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management. Piscataway: IEEE, 2015: 316-321. 10.1109/ablaze.2015.7155004
|
14 |
AlEBRAHIM S, AHMAD I. Task scheduling for heterogeneous computing systems [J]. The Journal of Supercomputing, 2017, 73(6): 2313-2338. 10.1007/s11227-016-1917-2
|
15 |
HEMAMALIMI M. Review on grid task scheduling in distributed heterogeneous environment [J]. International Journal of Computer Applications, 2012, 40(2): 24-30. 10.5120/4929-7159
|
16 |
吕向宇.面向异构多核的调度算法研究[D].武汉:武汉科技大学,2019:2-18. 10.22606/jaer.2020.51003
|
|
LYU X Y. Research on scheduling for heterogeneous multi-core processors [D]. Wuhan: Wuhan University of Science and Technology, 2019: 2-18. 10.22606/jaer.2020.51003
|
17 |
BLEUSE R, KEDAD-SIDHOUM S, MONNA F, et al. Scheduling independent tasks on multi-cores with GPU accelerators [J]. Concurrency and Computation: Practice and Experience, 2015, 27(6): 1625-1638. 10.1002/cpe.3359
|
18 |
KERRISK M. Cgroups(7)-Linux manual page [EB/OL]. [2021-03-22]. .
|
19 |
FUKUTOMI D, IIDA Y, AZUMIT, et al. GPUhd: augmenting YARN with GPU resource management [C]// Proceedings of the 2018 International Conference on High Performance Computing in Asia-pacific Region. New York: ACM, 2018: 127-136. 10.1145/3149457.3155313
|
20 |
HINDMAN B, KONWINSKI A, ZAHARIA M, et al. Mesos: platform for fine-grained resource sharing in the data center [C]// Proceedings of the 2011 8th USENIX Symposium on Networked Systems Design and Implementation. Berkeley: USENIX Association, 2011: 295-308.
|
21 |
NVIDIA Corporation. CUDA samples [EB/OL]. [2021-08-02]. .
|