Tools and methodology for performance analysis of large language models training on GPUs
This master thesis presents a tool, a methodology, a set of metrics, and practical examples for evaluating the performance of training large language models in HPC environments. NSYS2PRV is a tool that converts NVIDIA Nsight Systems reports into traces compatible with Paraver, enabling significantly...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:upcommonspor::bdf4723b76e74b654b7915a6216ed512 |
| Acceso en línea: | https://hdl.handle.net/2117/460885 |
| Access Level: | acceso abierto |
| Palabra clave: | Graphics processing units High performance computing Artificial intelligence Computaciço d'alt rendiment Eines de rendiment Intel·ligència artificial Anàlisi de rendiment Visualització de dades Unitats de processament gràfic Models de llenguatge High-performance computing Performance tools Performance analysis Data visualization Large language models Scalability Processadors gràfics Càlcul intensiu (Informàtica) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial |
| Sumario: | This master thesis presents a tool, a methodology, a set of metrics, and practical examples for evaluating the performance of training large language models in HPC environments. 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 Paraver compatible metrics that guide the analysis process, and we showcase examples where these metrics were successfully applied to real-world AI and HPC workloads. |
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