Open Access
| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01183 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/epjconf/202533701183 | |
| Published online | 07 October 2025 | |
- Martín Abadi, Paul Barham, Jianmin Chen et al. 2016. TensorFlow: a system for large-scale machine learning. In Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation (OSDI’16). USENIX Association, USA, 265–283.https://dl.acm.org/doi/10.5555/3026877.3026899 [Google Scholar]
- Adam Paszke, Sam Gross, Francisco Massa et al. 2019. PyTorch: an imperative style, high-performance deep learning library. Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 721, 8026–8037. https://dl.acm.org/doi/10.5555/3454287.3455008 [Google Scholar]
- The ONNX Community. ONNX: Open Neural Network Exchange - Open standard for machine learning interoperability. GitHub repository. https://github.com/onnx/onnx. [Google Scholar]
- Microsoft. ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator. GitHub repository. https://github.com/microsoft/onnxruntime. [Google Scholar]
- NVIDIA. TensorRT: SDK for high-performance deep learning inference on NVIDIA GPUs. GitHub repository. https://github.com/NVIDIA/TensorRT. [Google Scholar]
- AMD. ROCm. GitHub repository. https://github.com/ROCm/ROCm. [Google Scholar]
- An S et al (2023) C++ code generation for fast inference of deep learning models in root/tmva. J Phys Conf Ser 2438:012013. https://doi.org/10.1088/1742-6596/2438/1/012013 [Google Scholar]
- A. Hoecker, P. Speckmayer, J. Stelzer et al., TMVA - Toolkit for Multivariate Data Analysis (2007), physics/0703039. https://github.com/root-project/root/tree/master/tmva [Google Scholar]
- Rene Brun and Fons Rademakers, ROOT - An Object Oriented Data Analysis Framework, Proceedings AIHENP’96 Workshop, Lausanne, Sep. 1996, Nucl. Inst. & Meth. in Phys. Res. A 389 (1997) 81-86. https://doi.org/10.5281/zenodo.3895860 [CrossRef] [Google Scholar]
- Ioanna-Maria Panagou, Nikolaos Bellas, Lorenzo Moneta et al. 2024. Accelerating Machine Learning Inference on GPUs with SYCL. In Proceedings of the 12th International Workshop on OpenCL and SYCL (IWOCL ’24). Association for Computing Machinery, New York, NY, USA, Article 17, 1–2. https://doi.org/10.1145/3648115.3648123 [Google Scholar]
- Intel, Intel oneAPI, https://www.intel.com/content/www/us/en/developer/tools/oneapi/overview.html. [Google Scholar]
- Codeplay Software. portBLAS: Implementation of BLAS using the SYCL open standard. GitHub repository. https://github.com/codeplaysoftware/portBLAS. [Google Scholar]
- OpenMathLib. OpenBLAS: Optimized BLAS library based on GotoBLAS2 1.13 BSD version. GitHub repository. https://github.com/OpenMathLib/OpenBLAS. [Google Scholar]
- NVIDIA Corporation. (2022). Matrix Multiplication Background. Retrieved from https://docs.nvidia.com/deeplearning/performance/pdf/Matrix-Multiplication-Background-User-Guide.pdf [Google Scholar]
- S. Agostinelli and others. 2003. GEANT4 - A Simulation Toolkit. Nucl. Instrum. Meth. A 506, (2003), 250–303. https://doi.org/10.1016/S0168-9002(03)01368-8 [Google Scholar]
- He, Kaiming, et al. ‘Deep Residual Learning for Image Recognition’. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778, https://doi.org/10.1109/CVPR.2016.90. [Google Scholar]
- Qu, Huilin, and Loukas Gouskos. ‘Jet Tagging via Particle Clouds’. Physical Review D, vol. 101, no. 5, American Physical Society (APS), Mar. 2020, https://doi.org/10.1103/physrevd.101.056019. [Google Scholar]
- Google DeepMind. Graph Nets library: Build Graph Nets in Tensorflow. GitHub repository. https://github.com/google-deepmind/graph_nets. [Google Scholar]
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