RBF Volume Ray Casting on Multicore and Manycore CPUs
Modern supercomputers enable increasingly large N‐body simulations using unstructured point data. The structures implied by these points can be reconstructed implicitly. Direct volume rendering of radial basis function (RBF) kernels in domain‐space offers flexible classification and robust feature r...
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| Published in | Computer graphics forum Vol. 33; no. 3; pp. 71 - 80 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2014
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Modern supercomputers enable increasingly large N‐body simulations using unstructured point data. The structures implied by these points can be reconstructed implicitly. Direct volume rendering of radial basis function (RBF) kernels in domain‐space offers flexible classification and robust feature reconstruction, but achieving performant RBF volume rendering remains a challenge for existing methods on both CPUs and accelerators. In this paper, we present a fast CPU method for direct volume rendering of particle data with RBF kernels. We propose a novel two‐pass algorithm: first sampling the RBF field using coherent bounding hierarchy traversal, then subsequently integrating samples along ray segments. Our approach performs interactively for a range of data sets from molecular dynamics and astrophysics up to 82 million particles. It does not rely on level of detail or subsampling, and offers better reconstruction quality than structured volume rendering of the same data, exhibiting comparable performance and requiring no additional preprocessing or memory footprint other than the BVH. Lastly, our technique enables multi‐field, multi‐material classification of particle data, providing better insight and analysis. |
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| AbstractList | Modern supercomputers enable increasingly large N‐body simulations using unstructured point data. The structures implied by these points can be reconstructed implicitly. Direct volume rendering of radial basis function (RBF) kernels in domain‐space offers flexible classification and robust feature reconstruction, but achieving performant RBF volume rendering remains a challenge for existing methods on both CPUs and accelerators. In this paper, we present a fast CPU method for direct volume rendering of particle data with RBF kernels. We propose a novel two‐pass algorithm: first sampling the RBF field using coherent bounding hierarchy traversal, then subsequently integrating samples along ray segments. Our approach performs interactively for a range of data sets from molecular dynamics and astrophysics up to 82 million particles. It does not rely on level of detail or subsampling, and offers better reconstruction quality than structured volume rendering of the same data, exhibiting comparable performance and requiring no additional preprocessing or memory footprint other than the BVH. Lastly, our technique enables multi‐field, multi‐material classification of particle data, providing better insight and analysis. Modern supercomputers enable increasingly large N-body simulations using unstructured point data. The structures implied by these points can be reconstructed implicitly. Direct volume rendering of radial basis function (RBF) kernels in domain-space offers flexible classification and robust feature reconstruction, but achieving performant RBF volume rendering remains a challenge for existing methods on both CPUs and accelerators. In this paper, we present a fast CPU method for direct volume rendering of particle data with RBF kernels. We propose a novel two-pass algorithm: first sampling the RBF field using coherent bounding hierarchy traversal, then subsequently integrating samples along ray segments. Our approach performs interactively for a range of data sets from molecular dynamics and astrophysics up to 82 million particles. It does not rely on level of detail or subsampling, and offers better reconstruction quality than structured volume rendering of the same data, exhibiting comparable performance and requiring no additional preprocessing or memory footprint other than the BVH. Lastly, our technique enables multi-field, multi-material classification of particle data, providing better insight and analysis. [PUBLICATION ABSTRACT] |
| Author | Navratil, Paul Bowen, Anne Knoll, Aaron Gaither, Kelly Papka, Michael E. Reda, Khairi Wald, Ingo |
| Author_xml | – sequence: 1 givenname: Aaron surname: Knoll fullname: Knoll, Aaron organization: Texas Advanced Computing Center, University of Texas, Austin – sequence: 2 givenname: Ingo surname: Wald fullname: Wald, Ingo organization: Intel Corporation – sequence: 3 givenname: Paul surname: Navratil fullname: Navratil, Paul organization: Texas Advanced Computing Center, University of Texas, Austin – sequence: 4 givenname: Anne surname: Bowen fullname: Bowen, Anne organization: Texas Advanced Computing Center, University of Texas, Austin – sequence: 5 givenname: Khairi surname: Reda fullname: Reda, Khairi organization: Electronic Visualization Laboratory, University of Illinois, Chicago – sequence: 6 givenname: Michael E. surname: Papka fullname: Papka, Michael E. organization: Argonne National Laboratory – sequence: 7 givenname: Kelly surname: Gaither fullname: Gaither, Kelly organization: Texas Advanced Computing Center, University of Texas, Austin |
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| Cites_doi | 10.1109/TVCG.2007.70566 10.1109/TVCG.2009.158 10.1145/2535571.2535594 10.1615/Int.J.UncertaintyQuantification.2012003950 10.1007/BF01900346 10.1109/TVCG.2010.148 10.1109/TVCG.2009.142 10.1111/1467-8659.00508 10.1088/0067-0049/192/1/9 10.1109/InPar.2012.6339601 10.1109/LDAV.2013.6675159 10.1016/j.jmgm.2010.06.010 10.1145/1189762.1206075 10.1145/2145816.2145825 10.1145/2601097.2601199 10.1109/PACIFICVIS.2011.5742355 10.1109/TVCG.2007.1059 10.1145/383259.383300 10.1109/2945.468400 10.1146/annurev.aa.30.090192.002551 10.1109/VISUAL.2001.964490 10.1088/1367-2630/10/12/125006 10.1071/AS07022 10.1111/j.1365-2966.2008.13212.x |
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| Copyright | 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2014 The Eurographics Association and John Wiley & Sons Ltd. |
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Visualization and Computer Graphics, IEEE Transactions on 15, 6 (2009), 1579-5186. 2 – reference: Wald I., Boulos S., Shirley P.: Ray tracing deformable scenes using dynamic bounding volume hierarchies. ACM Transactions on Graphics (TOG) 26, 1 (2007), 6. 4, 6 – reference: Samet H.: The design and analysis of spatial data structures, vol. 199. Addison-Wesley Reading, MA, 1990. 4 – reference: Altay G., Croft R.A., Pelupessy I.: SPHRAY: a smoothed particle hydrodynamics ray tracer for radiative transfer. Monthly Notices of the Royal Astronomical Society 386, 4 (2008), 1931-1946. 3 – reference: Wald I., Slusallek P., Benthin C., Wagner M.: Interactive Rendering with Coherent Ray Tracing. Computer Graphics Forum (Proceedings of EUROGRAPHICS) 20, 3 (2001), 153-164. 4 – reference: Sigg C., Hadwiger M.: Fast third-order texture filtering. 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| Snippet | Modern supercomputers enable increasingly large N‐body simulations using unstructured point data. The structures implied by these points can be reconstructed... Modern supercomputers enable increasingly large N-body simulations using unstructured point data. The structures implied by these points can be reconstructed... |
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| SubjectTerms | Algorithms Analysis Categories and Subject Descriptors (according to ACM CCS) Central processing units Classification Computer graphics I.3.2 [Computer Graphics]: Graphics Systems-Distributed/network graphics I.3.3 [Computer Graphics]: Picture/Image Generation-Line and curve generation I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism-Raytracing Kernels Molecular dynamics Reconstruction Rendering Sampling Studies Supercomputers |
| Title | RBF Volume Ray Casting on Multicore and Manycore CPUs |
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