A Profile-Based AI-Assisted Dynamic Scheduling Approach for Heterogeneous Architectures
Author:
Funder
National Science Foundation
Publisher
Springer Science and Business Media LLC
Subject
Information Systems,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s10766-021-00721-2.pdf
Reference48 articles.
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2. Amaris, M., Cordeiro, D., Goldman, A., De Camargo, R.Y.: A simple BSP-based model to predict execution time in GPU applications. In: 2015 IEEE 22nd International Conference on High Performance Computing (HiPC), pp. 285–294 (2015). https://doi.org/10.1109/HiPC.2015.34
3. Arteaga, J., Zuckerman, S., Gao, G.R.: Generating fine-grain multithreaded applications using a multigrain approach. ACM Trans. Archit. Code Optim. 14(4), 47:1-47:26 (2017). https://doi.org/10.1145/3155288
4. Barnes, B.J., Rountree, B., Lowenthal, D.K., Reeves, J., de Supinski, B., Schulz, M.: A regression-based approach to scalability prediction. In: Proceedings of the 22Nd Annual International Conference on Supercomputing, ICS ’08, pp. 368–377. ACM, New York, NY, USA (2008). https://doi.org/10.1145/1375527.1375580
5. Belviranli, M.E., Bhuyan, L.N., Gupta, R.: A dynamic self-scheduling scheme for heterogeneous multiprocessor architectures. ACM Trans. Archit. Code Optim. 9(4), 57:1-57:20 (2013). https://doi.org/10.1145/2400682.2400716
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