A machine learning-based resource-efficient task scheduler for heterogeneous computer systems
Author:
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s11227-023-05266-4.pdf
Reference38 articles.
1. Tsog N, Becker M, Bruhn F, Behnam M, Sjodin M (2019) Static allocation of parallel tasks to improve schedulability in CPU-GPU heterogeneous real-time systems, in: IECON Proceedings (Industrial Electronics Conference), pp. 4516–4522, https://doi.org/10.1109/IECON.2019.8926767.
2. Wen Y, Wang Z, O’Boyle MFP (2014) Smart multi-task scheduling for Open CL programs on CPU/GPU heterogeneous platforms. https://doi.org/10.1109/HiPC.2014.7116910.
3. Ahmed U, Lin JCW, Srivastava G, Aleem M (2021) A load balance multi-scheduling model for OpenCL kernel tasks in an integrated cluster. Soft Comput 25(1):407–420. https://doi.org/10.1007/s00500-020-05152-8
4. Khalid YN, Aleem M, Prodan R, Iqbal MA, Islam MA (2018) E-OSched: a load balancing scheduler for heterogeneous multicores. J Supercomput 74(10):5399–5431. https://doi.org/10.1007/s11227-018-2435-1
5. Ahmed U, Aleem M, Noman Khalid Y, Arshad Islam M, Azhar Iqbal M, RALB-HC: a resource-aware load balancer for heterogeneous cluster, in: Concurrency And Computation: Practice and Experience,https://doi.org/10.1002/cpe.5606.
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