Skip to content

SC25 Talks at the UMD Booth

SC25 Talks at the UMD Booth

Understanding GPU Utilization Using LDMS Data on Perlmutter

GPGPU-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.

Speaker Bio - Onur Cankur

Onur Cankur

Onur Cankur is a Computer Science PhD student at the University of Maryland. He is working on high-performance computing (HPC) at Parallel Software and Systems Group with Abhinav Bhatele. His main interest is in performance analysis and modeling of large-scale parallel applications. He is also interested in resource usage analysis and modeling of HPC systems.