CARMA: Collocation-Aware Resource Manager
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917287230963712 |
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| author | Yousefzadeh-Asl-Miandoab, Ehsan Ciorba, Florina M. Tözün, Pınar |
| author_facet | Yousefzadeh-Asl-Miandoab, Ehsan Ciorba, Florina M. Tözün, Pınar |
| contents | GPUs running deep learning (DL) workloads are frequently underutilized. Collocating multiple DL training tasks on the same GPU can improve utilization but introduces two key risks: (1) out-of-memory (OOM) crashes for newly scheduled tasks, and (2) severe performance interference among co-running tasks, which can negate any throughput gains. These issues reduce system robustness, quality of service, and energy efficiency. We present CARMA, a task-level, collocation-aware resource manager for the server-scale. CARMA addresses collocation challenges via (1) fine-grained monitoring and bookkeeping of GPUs and a collocation risk analysis that filters out the high-risk GPUs; (2) task placement policies that cap GPU utilization to limit OOMs and interference; (3) integration of GPU memory need estimators for DL tasks to minimize OOMs during collocation; and (4) a lightweight recovery method that relaunches jobs crashed due to OOMs. Our evaluation on a DL training workload derived from real-world traces shows that CARMA uses GPUs more efficiently by making more informed collocation decisions: for the best-performing collocation policy, CARMA increases GPU streaming multiprocessor (SM) utilization by 54%, the parallelism achieved per SM by 61%, and memory use by 62%. This results in a ~35% and ~15% reduction in the end-to-end execution time (makespan) and GPU energy consumption, respectively, for this workload. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19073 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CARMA: Collocation-Aware Resource Manager Yousefzadeh-Asl-Miandoab, Ehsan Ciorba, Florina M. Tözün, Pınar Distributed, Parallel, and Cluster Computing Machine Learning Performance GPUs running deep learning (DL) workloads are frequently underutilized. Collocating multiple DL training tasks on the same GPU can improve utilization but introduces two key risks: (1) out-of-memory (OOM) crashes for newly scheduled tasks, and (2) severe performance interference among co-running tasks, which can negate any throughput gains. These issues reduce system robustness, quality of service, and energy efficiency. We present CARMA, a task-level, collocation-aware resource manager for the server-scale. CARMA addresses collocation challenges via (1) fine-grained monitoring and bookkeeping of GPUs and a collocation risk analysis that filters out the high-risk GPUs; (2) task placement policies that cap GPU utilization to limit OOMs and interference; (3) integration of GPU memory need estimators for DL tasks to minimize OOMs during collocation; and (4) a lightweight recovery method that relaunches jobs crashed due to OOMs. Our evaluation on a DL training workload derived from real-world traces shows that CARMA uses GPUs more efficiently by making more informed collocation decisions: for the best-performing collocation policy, CARMA increases GPU streaming multiprocessor (SM) utilization by 54%, the parallelism achieved per SM by 61%, and memory use by 62%. This results in a ~35% and ~15% reduction in the end-to-end execution time (makespan) and GPU energy consumption, respectively, for this workload. |
| title | CARMA: Collocation-Aware Resource Manager |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning Performance |
| url | https://arxiv.org/abs/2508.19073 |