CARMA: Collocation-Aware Resource Manager

• URL: http://arxiv.org/abs/2508.19073v2
• Date: Sat, 01 Nov 2025 16:13:11 GMT
• Title: CARMA: Collocation-Aware Resource Manager
• Authors: Ehsan Yousefzadeh-Asl-Miandoab, Reza Karimzadeh, Bulat Ibragimov, Florina M. Ciorba, Pınar Tözün,
• Abstract summary: Collocating multiple deep learning (DL) training tasks on the same GPU can improve utilization but introduces two key risks.<n>We present CARMA, a task-level, collocation-aware resource management system for the server-scale.
• Score: 5.998463702026698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 management system 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 avoid OOMs and limit 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 $\sim$35% and $\sim$15% reduction in the end-to-end execution time (makespan) and GPU energy consumption, respectively, for this workload.

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