NSYS2PRV: Detailed and Quantitative Analysis of Large-Scale GPU Execution Traces with Paraver

This work presents a tool, a methodology, a set of metrics, and practical examples for evaluating the performance of large-scale AI and traditional HPC applications using GPUs. NSYS2PRV is a tool that converts NVIDIA Nsight Systems reports into traces compatible with Paraver, enabling significantly...

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Veröffentlicht in:Proceedings / IEEE International Conference on Cluster Computing S. 1 - 12
Hauptverfasser: Clasca, Marc, Labarta, Jesus, Garcia-Gasulla, Marta
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 02.09.2025
Schlagworte:
Artificial intelligence
Computational efficiency
Data visualization
Graphics processing units
High performance computing
Measurement
Microscopy
Performance analysis
performance tools
Scalability
Statistical analysis
ISSN:2168-9253
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Zusammenfassung:This work presents a tool, a methodology, a set of metrics, and practical examples for evaluating the performance of large-scale AI and traditional HPC applications using GPUs. NSYS2PRV is a tool that converts NVIDIA Nsight Systems reports into traces compatible with Paraver, enabling significantly enhanced insight compared to current performance analysis practices. By leveraging the capabilities of a well-established HPC performance analysis tool, we enable the comparison of execution traces and the quantification of microscopic-level differences to explain behaviors across hundreds or more computing devices. We argue that large-scale GPU applications and AI workloads can greatly benefit from the type of large-scale performance analysis introduced here, an approach that is not yet widely adopted in this domain. Translating nsys-generated traces to Paraver allows analysts to combine the fine-grained, highly accurate execution data obtainable from proprietary tools with the flexibility and scalability of an open-source, parallel performance analysis environment. Paraver also enables easy, customizable computation of efficiency metrics. This work demonstrates a more effective and insightful analysis experience than that offered by the native visualization tools in Nsight Systems. Additionally, we introduce a set of Paravercompatible metrics that guide the analysis process, and we showcase examples where these metrics were successfully applied to real-world AI and HPC workloads.
ISSN:2168-9253
DOI:10.1109/CLUSTER59342.2025.11186477