Plenty of Room in the Middle: Revealing Opportunities on a GPU Cluster via Fine-Grained Monitoring
Read the original on arXiv AI →The paper presents a fine‑grained monitoring study of a university GPU cluster, collecting data at 100 ms intervals using a lightweight tool built on Nvidia’s DCGM library. Analysis of a 10‑day period with 7,399 jobs shows that over 70 % of jobs use less than 20 % of GPU memory and 50 % have below 5 % SM utilization, while 80 % of jobs experience a large mismatch between requested and actual runtimes. The authors also observe highly periodic communication patterns in multi‑GPU jobs, indicating that significant performance gains could be achieved through improved scheduling and multitasking rather than hardware upgrades.
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