GPGPU-based clusters and supercomputers have become extremely popular in the last ten years. There is a large number of GPGPU hardware counters exposed to the users, however, very little analysis has been done regarding insights they might offer about workloads running on them. In this work, we address this gap by analyzing previously unexplored GPU hardware counters collected via Lightweight Distributed Metric Service on Perlmutter, a leadership-class supercomputer. We examine several hardware counters related to utilization of GPU cores and memory and present a detailed spatial and temporal analysis of GPU workloads. We investigate spatial imbalance -- uneven GPU usage across multiple GPUs within a job. Our temporal study examines how GPU usage fluctuates during a job's lifetime, introducing two new metrics -- burstiness (the irregularity of large utilization changes) and temporal imbalance (deviations from mean utilization over time). Additionally, we compare machine learning and traditional high performance computing jobs. Our findings uncover inefficiencies and imbalances that can inform workload optimization and future HPC system design.

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