Graph Neural Network-based Tracking as a Service
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arXiv
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| Autori principali: | , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866917590776938496 |
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| author | Zhao, Haoran Naylor, Andrew Hsu, Shih-Chieh Calafiura, Paolo Farrell, Steven Feng, Yongbing Harris, Philip Coleman Khoda, Elham E Mccormack, William Patrick Rankin, Dylan Sheldon Ju, Xiangyang |
| author_facet | Zhao, Haoran Naylor, Andrew Hsu, Shih-Chieh Calafiura, Paolo Farrell, Steven Feng, Yongbing Harris, Philip Coleman Khoda, Elham E Mccormack, William Patrick Rankin, Dylan Sheldon Ju, Xiangyang |
| contents | Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelerate the inference time. Additionally, the large input graph size demands a large device memory for efficient computation, a requirement not met by all computing facilities used for particle physics experiments, particularly those lacking advanced GPUs. Furthermore, deploying the GNN-based track-finding algorithm in a production environment requires the installation of all dependent software packages, exclusively utilized by this algorithm. These computing challenges must be addressed for the successful implementation of GNN-based track-finding algorithm into production settings. In response, we introduce a ``GNN-based tracking as a service'' approach, incorporating a custom backend within the NVIDIA Triton inference server to facilitate GNN-based tracking. This paper presents the performance of this approach using the Perlmutter supercomputer at NERSC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09633 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Graph Neural Network-based Tracking as a Service Zhao, Haoran Naylor, Andrew Hsu, Shih-Chieh Calafiura, Paolo Farrell, Steven Feng, Yongbing Harris, Philip Coleman Khoda, Elham E Mccormack, William Patrick Rankin, Dylan Sheldon Ju, Xiangyang Computational Physics High Energy Physics - Experiment Data Analysis, Statistics and Probability Recent studies have shown promising results for track finding in dense environments using Graph Neural Network (GNN)-based algorithms. However, GNN-based track finding is computationally slow on CPUs, necessitating the use of coprocessors to accelerate the inference time. Additionally, the large input graph size demands a large device memory for efficient computation, a requirement not met by all computing facilities used for particle physics experiments, particularly those lacking advanced GPUs. Furthermore, deploying the GNN-based track-finding algorithm in a production environment requires the installation of all dependent software packages, exclusively utilized by this algorithm. These computing challenges must be addressed for the successful implementation of GNN-based track-finding algorithm into production settings. In response, we introduce a ``GNN-based tracking as a service'' approach, incorporating a custom backend within the NVIDIA Triton inference server to facilitate GNN-based tracking. This paper presents the performance of this approach using the Perlmutter supercomputer at NERSC. |
| title | Graph Neural Network-based Tracking as a Service |
| topic | Computational Physics High Energy Physics - Experiment Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2402.09633 |