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Relevant bibliographies by topics / Photon tracing on GPU

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Journal articles

Academic literature on the topic 'Photon tracing on GPU'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "Photon tracing on GPU"

1

Blyth, Simon. "Opticks : GPU Optical Photon Simulation for Particle Physics using NVIDIA® OptiXTM." EPJ Web of Conferences 214 (2019): 02027. http://dx.doi.org/10.1051/epjconf/201921402027.

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Opticks is an open source project that integrates the NVIDIA OptiX GPU ray tracing engine with Geant4 toolkit based simulations. Massive parallelism brings drastic performance improvements with optical photon simulation speedup expected to exceed 1000 times Geant4 with workstation GPUs. Optical physics processes of scattering, absorption, scintillator reemission and boundary processes are implemented as CUDA OptiX programs based on the Geant4 implementations. Wavelength-dependent material and surface properties as well as inverse cumulative distribution functions for reemission are interleaved into GPU textures providing fast interpolated property lookup or wavelength generation. OptiX handles the creation and application of a choice of acceleration structures such as boundary volume hierarchies and the transparent use of multiple GPUs. A major recent advance is the implementation of GPU ray tracing of complex constructive solid geometry shapes, enabling automated translation of Geant4 geometries to the GPU without approximation. Using common initial photons and random number sequences allows the Opticks and Geant4 simulations to be run point-by-point aligned. Aligned running has reached near perfect equivalence with test geometries.

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2

Blyth, Simon. "Integration of JUNO simulation framework with Opticks: GPU accelerated optical propagation via NVIDIA® OptiX™." EPJ Web of Conferences 251 (2021): 03009. http://dx.doi.org/10.1051/epjconf/202125103009.

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Opticks is an open source project that accelerates optical photon simulation by integrating NVIDIA GPU ray tracing, accessed via NVIDIA OptiX, with Geant4 toolkit based simulations. A single NVIDIA Turing architecture GPU has been measured to provide optical photon simulation speedup factors exceeding 1500 times single threaded Geant4 with a full JUNO analytic GPU geometry automatically translated from the Geant4 geometry. Optical physics processes of scattering, absorption, scintillator reemission and boundary processes are implemented within CUDA OptiX programs based on the Geant4 implementations. Wavelength-dependent material and surface properties as well as inverse cumulative distribution functions for reemission are interleaved into GPU textures providing fast interpolated property lookup or wavelength generation. In this work we describe major recent developments to facilitate integration of Opticks with the JUNO simulation framework including on GPU collection effciency hit culling which substantially reduces both the CPU memory needed for photon hits and copying overheads. Also progress with the migration of Opticks to the all new NVIDIA OptiX 7 API is described.

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3

Blyth, Simon. "Meeting the challenge of JUNO simulation with Opticks: GPU optical photon acceleration via NVIDIA® OptiXTM." EPJ Web of Conferences 245 (2020): 11003. http://dx.doi.org/10.1051/epjconf/202024511003.

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Opticks is an open source project that accelerates optical photon simulation by integrating NVIDIA GPU ray tracing, accessed via NVIDIA OptiX, with Geant4 toolkit based simulations. A single NVIDIA Turing architecture GPU has been measured to provide optical photon simulation speedup factors exceeding 1500 times single threaded Geant4 with a full JUNO analytic GPU geometry automatically translated from the Geant4 geometry. Optical physics processes of scattering, absorption, scintillator reemission and boundary processes are implemented within CUDA OptiX programs based on the Geant4 implementations. Wavelength-dependent material and surface properties as well as inverse cumulative distribution functions for reemission are interleaved into GPU textures providing fast interpolated property lookup or wavelength generation. Major recent developments enable Opticks to benefit from ray trace dedicated RT cores available in NVIDIA RTX series GPUs. Results of extensive validation tests are presented.

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4

Parker, Steven G., Heiko Friedrich, David Luebke, et al. "GPU ray tracing." Communications of the ACM 56, no. 5 (2013): 93–101. http://dx.doi.org/10.1145/2447976.2447997.

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5

Weiskopf, Daniel, Tobias Schafhitzel, and Thomas Ertl. "GPU-Based Nonlinear Ray Tracing." Computer Graphics Forum 23, no. 3 (2004): 625–33. http://dx.doi.org/10.1111/j.1467-8659.2004.00794.x.

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6

Hu, Wei, Yangyu Huang, Fan Zhang, Guodong Yuan, and Wei Li. "Ray tracing via GPU rasterization." Visual Computer 30, no. 6-8 (2014): 697–706. http://dx.doi.org/10.1007/s00371-014-0968-8.

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7

Pérard-Gayot, Arsène, Javor Kalojanov, and Philipp Slusallek. "GPU Ray Tracing using Irregular Grids." Computer Graphics Forum 36, no. 2 (2017): 477–86. http://dx.doi.org/10.1111/cgf.13142.

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8

Couturier, David, and Michel R. Dagenais. "LTTng CLUST: A System-Wide Unified CPU and GPU Tracing Tool for OpenCL Applications." Advances in Software Engineering 2015 (August 19, 2015): 1–14. http://dx.doi.org/10.1155/2015/940628.

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As computation schemes evolve and many new tools become available to programmers to enhance the performance of their applications, many programmers started to look towards highly parallel platforms such as Graphical Processing Unit (GPU). Offloading computations that can take advantage of the architecture of the GPU is a technique that has proven fruitful in recent years. This technology enhances the speed and responsiveness of applications. Also, as a side effect, it reduces the power requirements for those applications and therefore extends portable devices battery life and helps computing clusters to run more power efficiently. Many performance analysis tools such as LTTng, strace and SystemTap already allow Central Processing Unit (CPU) tracing and help programmers to use CPU resources more efficiently. On the GPU side, different tools such as Nvidia’s Nsight, AMD’s CodeXL, and third party TAU and VampirTrace allow tracing Application Programming Interface (API) calls and OpenCL kernel execution. These tools are useful but are completely separate, and none of them allow a unified CPU-GPU tracing experience. We propose an extension to the existing scalable and highly efficient LTTng tracing platform to allow unified tracing of GPU along with CPU’s full tracing capabilities.

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9

Liu, Baoquan, Li-Yi Wei, Xu Yang, et al. "Non-Linear Beam Tracing on a GPU." Computer Graphics Forum 30, no. 8 (2011): 2156–69. http://dx.doi.org/10.1111/j.1467-8659.2011.01905.x.

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10

Ohkawara, Masaru, Hideo Saito, and Issei Fujishiro. "Experiencing GPU path tracing in online courses." Graphics and Visual Computing 4 (June 2021): 200022. http://dx.doi.org/10.1016/j.gvc.2021.200022.

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