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A composition-free parallel volume rendering method

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Abstract

In the in situ visualization of large-scale simulation, if using the traditional sort-last parallel rendering method, the performance cannot be fully improved due to the heavy communication cost in the final stage: compositing of images. In order to resolve this problem, in this paper, we proposed a composition-free parallel volume rendering method. This is achieved by our proposed hybrid parallelization based on the image space partition and octree-based data space partition. Firstly, in the data distribution stage, an octree structure decoupled from volume representation was proposed, which only stored the indicts of sub-blocks, the adjacent sub-blocks, and the space coordinate information of its bounding box. Before rendering, to avoid the compositing stage, a ray-data intersection list was created for each pixel, which sorted by visibility order and identified NON-EMPTY sub-blocks along the ray. Then in the rendering stage, to improve the efficiency of rendering, a ray-data intersection lists-based rendering method was proposed. Finally, we only need to align the partial images to form the final output image. No compositing process is necessary in our parallel volume rendering method. In order to demonstrate the efficiency of our approach, two existing volume rendering methods have been carried out to compare with our composition-free method.

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References

  • Abraham F, Celes W, Cerqueira R, Campos JL (2004) A load-balancing strategy for sort-first distributed rendering. In: Proceedings of 17th Brazilian symposium on computer graphics and image processing, pp 292–299

  • Beddiaf A, Babahenini M (2015) An improved splat-based ray tracing for point-based objects. In: Proceedings of 2015 12th international symposium on programming and systems (ISPS), pp 1–5

  • Bethel E, Humphreys G, Paul B, Brederson JD (2003) Sort-first, distributed memory parallel visualization and rendering. In: Proceedings of IEEE symposium on parallel and large-data visualization and graphics, pp 41–50

  • Bi C, Fu B, Chen J, Zhao Y, Yang L, Duan Y, Shi Y (2019) Machine learning based fast multi-layer liquefaction disaster assessment. World Wide Web 22(5):1935–1950. https://doi.org/10.1007/s11280-018-0632-8

    Article  Google Scholar 

  • Bi C, Pan G, Yang L, Lin C, Hou M, Huang Y (2019) Evacuation route recommendation using auto-encoder and markov decision process. Appl Soft Comput 84:105741. https://doi.org/10.1016/j.asoc.2019.105741

    Article  Google Scholar 

  • Bi C, Wang J, Duan Y, Fu B, Kang J, Shi Y (2020) Mobilenet based apple leaf diseases identification. Mobile Netw Appl. https://doi.org/10.1007/s12650-019-00555-8

    Article  Google Scholar 

  • Bi C, Yang L, Duan Y, Shi Y (2019) A survey on visualization of tensor field. J Vis 22:1–20. https://doi.org/10.1007/s11036-020-01640-1

    Article  Google Scholar 

  • Brendan M, Marco A, Daniel W, Torsten M (2010) Sort-first parallel volume rendering. IEEE Trans Vis Comput Graphics 17(8):1164–1177

    Google Scholar 

  • Cao Y, Wang H, Ai Z (2014) Distributed multi-gpu accelerated hybrid parallel rendering for massively parallel environment. In: Proceedings of 2014 international conference on virtual reality and visualization, pp 30–36

  • Chang H, Lei X, Dai S (2009) A dynamic load balancing algorithm for sort-first rendering clusters. In: Proceedings of 2009 2nd IEEE international conference on computer science and information technology, pp 515–519

  • Duksu K (2016) Memory efficient parallel ray casting algorithm for unstructured grid volume rendering on multi-core cpus. J Kiise 43(3):304–313

    Article  Google Scholar 

  • Guitian J, Enrico G, Fabio M (2010) View-dependent exploration of massive volumetric models on large-scale light field displays. Vis Comput 26(6–8):1037–1047

    Google Scholar 

  • Guo L, Mei X, Gang P, Bingrong W (2009) Research of rendering anisotropic volume data directly based on the shear-warp algorithm. In: Proceedings of 2009 international conference on environmental science and information application technology, vol 2, pp 427–430

  • Hadwiger M, Al-Awami A, Beyer J, Agus M, Pfister H (2017) Sparseleap: Efficient empty space skipping for large-scale volume rendering. IEEE Trans Visual Comput Gr 24(1):974–983

    Article  Google Scholar 

  • Ji Z, He B (2011) A dynamic load balancing method for parallel rendering and physical simulation system based sort-first architecture. In: Proceedings of 2011 international conference on computer science and network technology, vol 3, pp 1792–1796

  • Jing G, Song W (2008) An octree ray casting algorithm based on multi-core cpus. In: Proceedings of 2008 international symposium on computer science and computational technology, vol 2, pp 783–787

  • Jung M, Park H, Paik D (1998) An analytical ray casting of volume data. In: Proceedings of pacific graphics ’98. Sixth pacific conference on computer graphics and applications (Cat. No.98EX208), pp 79–86

  • Lagae A, Dutre P (2008) Accelerating ray tracing using constrained tetrahedralizations. In: Proceedings of 2008 IEEE symposium on interactive ray tracing

  • Larsen M, Moreland K, Johnson CR, Childs H (2016) Optimizing multi-image sort-last parallel rendering. In: Proceedings of 2016 IEEE 6th symposium on large data analysis and visualization (LDAV), pp 37–46

  • Li J, Ma K (2020) P5: Portable progressive parallel processing pipelines for interactive data analysis and visualization. IEEE Trans Visual Comput Gr 26(1):1151–1160

    Google Scholar 

  • Li T, Xie M, Zhao W, Wei Y (2010) Shear-warp rendering algorithm based on radial basis functions interpolation. In: Proceedings of 2010 second international conference on computer modeling and simulation, vol 2, pp 425–429

  • Li J, Zhang W,Li X (2008) An adaptive refinement ratio algorithm for structured amr method based on samrai framework. In: Proceedings of 10th IEEE international conference on high performance computing and communications, pp 907–912

  • Ma K, Painter J, Hansen C, Krogh M (1994) Parallel volume rendering using binary-swap compositing. IEEE Comput Graphics Appl 14(4):59–68

    Article  Google Scholar 

  • Moloney B, Ament M, Weiskopf D, Moller T (2011) Sort-first parallel volume rendering. IEEE Trans Visual Comput Gr 17(8):1164–1177

    Article  Google Scholar 

  • Nonaka J, Bi C, Fujita M, Ono K (2014) 2-3-4 decomposition method for large-scale parallel image composition with arbitrary number of nodes. In: Proceedings of international conference on systems informatics, modelling and simulation, pp 59–64. https://doi.org/10.1109/SIMS.2014.21

  • Quan Y, Li W, Pang Y, Zhou G, Zhang Z (2006) A new point-based rendering method for ray tracing. In: Proceedings of 2006 international conference on machine learning and cybernetics, pp 3978–3981

  • Reichl F, Treib M, Westermann R (2013) Visualization of big sph simulations via compressed octree grids. In: Proceedings of 2013 IEEE international conference on big data, pp 71–78

  • Reshetov A (2006) Omnidirectional ray tracing traversal algorithm for kd-trees. In: Proceedings of 2006 IEEE symposium on interactive ray tracing, pp 57–60

  • Sano K, Kitajima H, Kobayashi H, Nakamura T (2000) Data-parallel volume rendering with adaptive volume subdivision. IEICE Trans Inf Syst 83(1):80–89

    Google Scholar 

  • Serina F, Madamba J (2011) Reservoir: an alternative load balancing technique for parallel ray tracing. In: Proceedings of 2011 fifth Asia modelling symposium, pp 193–197

  • Stephane M, Catherine M, Jean-Michel D (2006) Dynamic load balancing for parallel volume rendering. In: Proceedings of 6th eurographics conference on parallel graphics and visualization, pp 43–50

  • Tao L, Qian Z (2011) An improved fast ray casting volume rendering algorithm of medical image. In: Proceedings of 2011 4th international conference on biomedical engineering and informatics (BMEI), vol 1, pp 109–112

  • Tim B, Kilian W, Bernd H, Christoph G (2017) A task-based parallel rendering component for large-scale visualization applications. In: Proceedings of the eurographics symposium on parallel graphics and visualization (EGPGV), pp 63–71

  • Vincent V, Xing M, Philippe D (2008) Simple empty-space removal for interactive volume rendering. J Gr GPU & Game Tools 13(2):21–36

    Google Scholar 

  • Wang H, Ai Z, Cao Y, Xiao L (2016) A parallel preintegration volume rendering algorithm based on adaptive sampling. J Vis 19(3):437–446

    Article  Google Scholar 

  • Wang K, Shareef N, Shen H (2018) Image and distribution based volume rendering for large data sets. In: Proceedings of 2018 IEEE pacific visualization symposium (PacificVis), pp 26–35

  • Wang Y, Dou W, Constans J (2012) Accelerating volume ray casting by empty space skipping used for computer-aided therapy. In: Proceedings of 2012 international conference on audio, language and image processing, pp 661–667

  • Wang H, Cao Y, Xiao L, Wu G(2013) A parallel adaptive volume rendering algorithm based on information entropy. In: Proceedings of the 2013 international conference on virtual reality and visualization, ICVRV ’13, pp 127-131, USA, 2013. IEEE Computer Society

  • Wang P, Liu H, Li S, Zeng L, Cai X (2011) Multi-gpu compositeless parallel rendering algorithm. In: Proceedings of 2011 12th international conference on computer-aided design and computer graphics, pp 103–107

  • Westenberg M, Roerdink J (2000) X-ray volume rendering by hierarchical wavelet splatting. In: Proceedings of 15th international conference on pattern recognition. ICPR-2000, vol 3, pp 159–162

  • Xue J, Yao J, Lu K, Shao L, Rahman M (2016) Efficient volume rendering methods for out-of-core datasets by semi-adaptive partitioning. Inf Sci 370:463–475

    Article  Google Scholar 

  • Xue J, Zhu X, Lu K, Kou Y (2019) Parallel volume rendering method for out-of-core non-uniformly partitioned datasets. In: Proceedings of 2019 IEEE international conference on multimedia & expo workshops (ICME), pp 599–60

  • Yang L, Xie P, Bi C, Zhang R, Cai e B, Shao X, Wang R (2020) Household power consumption pattern modeling through a single power sensor. Renewable Energy 155:121–133. https://doi.org/10.1016/j.renene.2020.03.118

    Article  Google Scholar 

  • Yao J, Xue J, Lv K, Miao Q (2016) A parallel volume rendering method for massive data. In: Proceedings of 2016 IEEE international conference on multimedia expo workshops (ICMEW), pp 1–6

  • Yu H, Wang C, Ma K (2008) Massively parallel volume rendering using 2–3 swap image compositing. In: Proceedings of 2008 ACM/IEEE conference on supercomputing, pp 1–11

  • Zhao Y, Luo X, Lin X, Wang H, Kui X, Zhou F, Wang J, Chen Y, Chen W (2020) Visual analytics for electromagnetic situation awareness in radio monitoring and management. IEEE Trans Visual Comput Gr 26(1):590–600. https://doi.org/10.1109/TVCG.2019.2934655

    Article  Google Scholar 

  • Zhou F, Lin X, Liu C, Zhao Y, Xu P, Ren L, Xue T, Ren L (2019) A survey of visualization for smart manufacturing. J Vis 22(2):419–435. https://doi.org/10.1007/s12650-018-0530-2

    Article  Google Scholar 

  • Zhu H, Chan K, Wang L, Cai W (2003) Dpbp: a sort-first parallel rendering algorithm for distributed rendering environments. In: Proceedings of 2003 international conference on cyberworlds, pp 214–220

Download references

Acknowledgements

This work was partially supported by the National Numerical Windtunnel Project, partially by the National Natural Science Foundation of China under Grant No. 61702360.

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Jiamin Wang & Chongke Bi

  2. Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang, China

    Liang Deng, Fang Wang, Yang Liu & Yueqing Wang

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  1. Jiamin Wang
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  2. Chongke Bi
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  3. Liang Deng
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  4. Fang Wang
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  5. Yang Liu
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Correspondence to Chongke Bi.

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Wang, J., Bi, C., Deng, L. et al. A composition-free parallel volume rendering method. J Vis 24, 531–544 (2021). https://doi.org/10.1007/s12650-020-00719-x

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