Open Access
| Issue |
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
|
|
|---|---|---|
| Article Number | 03009 | |
| Number of page(s) | 10 | |
| Section | Offline Computing | |
| DOI | https://doi.org/10.1051/epjconf/202125103009 | |
| Published online | 23 August 2021 | |
- Opticks Repository, https://bitbucket.org/simoncblyth/opticks/ [Google Scholar]
- Opticks References, https://simoncblyth.bitbucket.io [Google Scholar]
- Opticks Group, https://groups.io/g/opticks [Google Scholar]
- S. Blyth, EPJ Web Conf. 245, 11003 (2020) https://doi.org/10.1051/epjconf/202024511003 [Google Scholar]
- S. Blyth, EPJ Web Conf. 214, 02027 (2019) https://doi.org/10.1051/epjconf/201921402027 [Google Scholar]
- Blyth Simon C 2017 J. Phys.: Conf. Ser. 898 042001 https://doi.org/10.1088/1742-6596/898/4/042001 [CrossRef] [Google Scholar]
- S. Agostinelli, J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Arce et al., Nucl. Instrum. Methods. Phys. Res. A 506, 250 (2003) [Google Scholar]
- J. Allison, K. Amako, J. Apostolakis, H. Araujo, P. Dubois, M. Asai et al., IEEE Trans Nucl Sci, 53, 270 (2006) [Google Scholar]
- J. Allison, K. Amako, J. Apostolakis, P. Arce, M. Asai, T. Aso et al., Nucl. Instrum. Methods. Phys. Res. A 835, 186 (2016) [Google Scholar]
- OptiX: a general purpose ray tracing engine S. Parker, J. Bigler, A. Dietrich, H. Friedrich, J. Hoberock et al., ACM Trans. Graph.: Conf. Series 29, 66 (2010) [Google Scholar]
- OptiX introduction, https://developer.nvidia.com/optix [Google Scholar]
- OptiX API, http://raytracing-docs.nvidia.com/optix/index.html [Google Scholar]
- OptiX7 https://developer.nvidia.com/blog/how-to-get-started-with-optix-7/ [Google Scholar]
- The Application of SNiPER to the JUNO Simulation, T. Lin et al., J.Phys.Conf.Ser. 898 042029 (2017) https://doi.org/10.1088/1742-6596/898/4/042029 [Google Scholar]
- Neutrino physics with JUNO F. An et al., J. Phys. G. 43, 030401 (2016) [Google Scholar]
- NVIDIARTX, https://developer.nvidia.com/rtx [Google Scholar]
- Understanding Throughput Oriented Architectures M. Garland, D.B. Kirk, Commun. ACM 53(11), 58 (2010) [Google Scholar]
- cuRAND, http://docs.nvidia.com/cuda/curand/index.html [Google Scholar]
- The NumPy array: a structure for efficient numerical computation S. Van der Walt, S. Colbert, G. Varoquaux, Comput. Sci. Eng. 13, 22 (2011) [Google Scholar]
- Chapter 26 - Thrust: A Productivity-Oriented Library for CUDA N. Bell, J. Hoberock, GPU Computing Gems Jade Edition, (2012), pp 359–371 [Google Scholar]
- Shared GPU/CPU “CSGFoundry” geometry model, https://github.com/simoncblyth/CSG [Google Scholar]
- Converter of Opticks/GGeo geometry to “CSGFoundry” model, https://github.com/simoncblyth/CSG_GGeo [Google Scholar]
- NVIDIA OptiX 7 and pre-7 renderer of “CSGFoundry” geometry, https://github.com/simoncblyth/CSGOptiX [Google Scholar]
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