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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings / IEEE International Conference on Cluster Computing s. 1 - 12
Hlavní autoři: Clasca, Marc, Labarta, Jesus, Garcia-Gasulla, Marta
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 02.09.2025
Témata:
Artificial intelligence
Computational efficiency
Data visualization
Graphics processing units
High performance computing
Measurement
Microscopy
Performance analysis
performance tools
Scalability
Statistical analysis
ISSN:2168-9253
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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