Orchestrated Co-scheduling, Resource Partitioning, and Power Capping on CPU-GPU Heterogeneous Systems via Machine Learning

Author:

Saba Issa,Arima Eishi,Liu Dai,Schulz Martin

Publisher

Springer International Publishing

Reference26 articles.

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3. Lecture Notes in Computer Science;E Arima,2020

4. Arima, E., et al.: Optimizing hardware resource partitioning and job allocations on modern gpus under power caps. In: ICPPW (2022)

5. Azimi, R., et al.: PowerCoord: a coordinated power capping controller for multi-CPU/GPU servers. In: IGSC, pp. 1–9 (2018)

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach;2023 IEEE International Conference on Cluster Computing (CLUSTER);2023-10-31

2. Evaluating the Potential of Coscheduling on High-Performance Computing Systems;Job Scheduling Strategies for Parallel Processing;2023

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