smCompactor
Author:
Affiliation:
1. Seoul National University, Seoul, South Korea
2. Chung-Ang University, Seoul, South Korea
3. Sookmyung Women's University, Seoul, South Korea
Funder
National Research Foundation of Korea (NRF) funded by the Ministry of Science
National Research Foundation of the Korea government (MSIT)
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3412841.3441989
Reference25 articles.
1. Slate: Enabling Workload-Aware Efficient Multiprocessing for Modern GPGPUs
2. Efficient scheduling of streams on GPGPUs;Roui Mohammad Beheshti;Journal of Supercomputing,2020
3. Google Cloud 2017. https://cloud.google.com/gpu Google Cloud 2017. https://cloud.google.com/gpu
Cited by 5 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. Application-aware Resource Sharing using Software and Hardware Partitioning on Modern GPUs;NOMS 2024-2024 IEEE Network Operations and Management Symposium;2024-05-06
2. Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach;2023 IEEE International Conference on Cluster Computing (CLUSTER);2023-10-31
3. ROSGM: A Real-Time GPU Management Framework with Plug-In Policies for ROS 2;2023 IEEE 29th Real-Time and Embedded Technology and Applications Symposium (RTAS);2023-05
4. Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps;Workshop Proceedings of the 51st International Conference on Parallel Processing;2022-08-29
5. K-Scheduler: dynamic intra-SM multitasking management with execution profiles on GPUs;Cluster Computing;2021-10-12
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