Presentation
Understanding GPU Utilization Using LDMS Data on Perlmutter
DescriptionGPGPU-based clusters and supercomputers have grown significantly in popularity over the past decade. While numerous GPGPU hardware counters are available to users, their potential for workload characterization remains underexplored. In this work, we analyze previously overlooked GPU hardware counters collected via the Lightweight Distributed Metric Service on Perlmutter. We examine spatial imbalance, defined as uneven GPU usage within the same job, and perform a temporal analysis of how counter values change during execution. Using temporal imbalance, we capture deviations from average usage over time. Our findings reveal inefficiencies and imbalances that can guide workload optimization and inform future HPC system design.
Event Type
Research and ACM SRC Posters
TimeThursday, 20 November 20258:00am - 5:00pm CST
LocationSecond Floor Atrium
Archive
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