Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

GPU-based adaptive data reconstruction for large-scale statistical visualization

  • Regular Paper
  • Published:
Journal of Visualization Aims and scope Submit manuscript

Abstract

Statistical data summarization can significantly reduce the data storage footprint for large-scale scientific simulations while maintaining data accuracy. However, the high-resolution reconstructed data causes a memory bottleneck in graphics processing unit (GPU)-based post-hoc visualization using limited graphics memory. In this paper, we propose a statistical summarization model-driven adaptive data reconstruction method for large-scale statistical visualization on GPUs. It uses the spatial Gaussian mixture model to iteratively compute the Shannon entropy on multi-level grids, driving an adaptive mesh refinement that retains complex physical features. A graphics shader-based data reconstruction algorithm is used to efficiently generate the scalar field on the adaptive grid while seamlessly integrating with GPU-accelerated rendering algorithms. The experimental tests used data generated by five real-world scientific simulations with a maximum grid resolution of 134 million. Qualitative and quantitative analysis results show that our method can achieve efficient and high-quality reconstruction of the statistical summary data on a GPU, and the maximum data compression ratio is close to two orders of magnitude.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

References

  • Bai ZH, Zhou ZG, Yang RF, Tao YB, Lin H (2015) Time-varying volume visualization and feature tracking on asymmetric gaussian function. J Image Gr 20(4):0576–0584

    Google Scholar 

  • Bailey M (2016) Opengl compute shaders. Oregon State University

    Google Scholar 

  • Ballester-Ripoll R, Steiner D, Pajarola R (2017) Multiresolution volume filtering in the tensor compressed domain. IEEE Trans Visual Comput Gr 24(10):2714–2727

    Article  Google Scholar 

  • Balsa Rodríguez M, Gobbetti E, Iglesias Guitian JA, Makhinya M, Marton F, Pajarola R, Suter SK (2014) State-of-the-art in compressed gpu-based direct volume rendering. In Computer Graphics Forum, vol 33, pp 77–100. Wiley Online Library https://doi.org/10.1111/cgf.12280

  • Berger MJ, Oliger J (1984) Adaptive mesh refinement for hyperbolic partial differential equations. J Comput Phys 53(3):484–512

    Article  MathSciNet  MATH  Google Scholar 

  • Burtscher M, Ratanaworabhan P (2008) Fpc: a high-speed compressor for double-precision floating-point data. IEEE Trans Comput 58(1):18–31

    Article  MathSciNet  MATH  Google Scholar 

  • Calhoun D, Burstedde C (2017) Forestclaw: a parallel algorithm for patch-based adaptive mesh refinement on a forest of quadtrees. arXiv preprint arXiv:1703.03116

  • Hank C, Eric B, Brad W, Jeremy M, Sean A, David P, Kathleen B, Mark M, Gunther HW, Hari K, Thomas F, Allen S, Christoph G, Bethel EW, David C, Oliver R, Marc D, Jean F, Paul N. Visit: An end-user tool for visualizing and analyzing very large data. Accessed 1 Nov 2012 https://www.osti.gov/biblio/1170761

  • Claggett S, Azimi S, Burtscher M (2018) Spdp: an automatically synthesized lossless compression algorithm for floating-point data. In: 2018 data compression conference, pp 335–344. IEEE

  • Crassin Cyril, Neyret Fabrice, Lefebvre Sylvain, Eisemann Elmar (2009) Gigavoxels: Ray-guided streaming for efficient and detailed voxel rendering. In: Proceedings of the 2009 symposium on Interactive 3D graphics and games, pp 15–22

  • Deering M (1995) Geometry compression. In Proceedings of the 22nd annual conference on Computer graphics and interactive techniques, pp 13–20

  • Dutta S, Chen C-M, Heinlein G, Shen H-W, Chen J-P (2016) In situ distribution guided analysis and visualization of transonic jet engine simulations. IEEE Trans Visual Comput Gr 23(1):811–820

    Article  Google Scholar 

  • Dutta S, Shen H-W (2015) Distribution driven extraction and tracking of features for time-varying data analysis. IEEE Trans Visual Comput Gr 22(1):837–846

    Article  Google Scholar 

  • Engel Klaus (2011) Cera-tvr: a framework for interactive high-quality teravoxel volume visualization on standard pcs. In 2011 IEEE symposium on large data analysis and visualization. IEEE, pp 123–124

  • Ginsburg D, Purnomo B, Shreiner D, Munshi A (2014) OpenGL ES 3.0 Programming Guide. OpenGL. Pearson Education. https://books.google.com/books?id=7qT0AgAAQBAJ

  • Guthe S, Strasser W (2004) Advanced techniques for high-quality multi-resolution volume rendering. Comput Gr 28(1):51–58

    Article  Google Scholar 

  • Hadwiger M, Beyer J, Jeong W-K, Pfister H (2012) Interactive volume exploration of petascale microscopy data streams using a visualization-driven virtual memory approach. IEEE Trans Visual Comput Gr 18(12):2285–2294

    Article  Google Scholar 

  • Harel G, Lekien J-B, Pébaÿ PP (2017) Visualization and analysis of large-scale, tree-based, adaptive mesh refinement simulations with arbitrary rectilinear geometry. arXiv preprint arXiv:1702.04852

  • Hoang D, Klacansky P, Bhatia H, Bremer P-T, Lindstrom P, Pascucci V (2018) A study of the trade-off between reducing precision and reducing resolution for data analysis and visualization. IEEE Trans Visual Comput Gr 25(1):1193–1203

    Article  Google Scholar 

  • Händel P (2018) Understanding normalized mean squared error in power amplifier linearization. IEEE Microwave Wirel Compon Lett 28(11):1047–1049

    Article  Google Scholar 

  • Khodakovsky A, Schröder P, Sweldens W (2000) Progressive geometry compression. In: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pp 271–278

  • Khronos Group, Inc. (2019) The OpenGL® Shading Language, Version 4.60.7. https://www.khronos.org/registry/OpenGL/specs/gl/GLSLangSpec.4.60.pdf

  • Khronos Group, Inc. (2019) The OpenGL® Graphics System: A Specification (Version 4.6 (Core Profile)). https://www.khronos.org/registry/OpenGL/specs/gl/glspec46.core.pdf

  • Kwan KC, Xuemiao X, Wan L, Wong T-T, Pang W-M (2017) Packing vertex data into hardware-decompressible textures. IEEE Trans Visual Comput Gr 24(5):1705–1716

    Article  Google Scholar 

  • Lakshminarasimhan S, Shah N, Ethier S, Seung-Hoe K, Chang C-S, Klasky S, Latham R, Ross R, Samatova NF (2013) Isabela for effective in situ compression of scientific data. Concurr Comput Pract Exp 25(4):524–540

    Article  Google Scholar 

  • Levoy M (1988) Display of surfaces from volume data. IEEE Comput Gr Appl 8(3):29–37

    Article  Google Scholar 

  • Liu Susen, Levine JA, Bremer P-T, Pascucci V (2012) Gaussian mixture model based volume visualization. In: IEEE symposium on large data analysis and visualization (LDAV), pp 73–77. IEEE

  • Losasso F, Gibou F, Fedkiw R (2004) Simulating water and smoke with an octree data structure. In ACM SIGGRAPH 2004 Papers, pp 457–462

  • Luebke D, Reddy M, Cohen J D, Varshney A, Watson B, Huebner R (2003) Level of detail for 3D graphics. Morgan Kaufmann

  • Merhav N, Bhaskaran V (1997) Fast algorithms for DCT-domain image downsampling and for inverse motion compensation. IEEE Trans Circuits Syst Video Technol 7(3):468–476

    Article  Google Scholar 

  • Pajarola R, Rossignac J (2000) Compressed progressive meshes. IEEE Trans Visual Comput Gr 6(1):79–93

    Article  Google Scholar 

  • Patchett J, Gisler G (2017) Deep water impact ensemble data set. https://sciviscontest2018.org/wp-content/uploads/sites/19/2017/09/DeepWaterImpactEnsembleDataSet_Revision1.pdf. 

  • Roche Antoine, Dubois Jérôme (2020) Evaluation of mesh compression and GPU ray casting for tree based AMR data in vtk. In: EuroVis (Posters), pp 5–7

  • Sara U, Akter M, Uddin MS (2019) Image quality assessment through FSIM, SSIM, MSE and PSNR-a comparative study. J Comput Commun 7(3):8–18

    Article  Google Scholar 

  • Sasaki N, Sato K, Endo T, Matsuoka S (2015) Exploration of lossy compression for application-level checkpoint/restart. In: 2015 IEEE international parallel and distributed processing symposium, pp 914–922. IEEE

  • Schive H-Y, Tsai Y-C, Chiueh T (2010) Gamer: a graphic processing unit accelerated adaptive-mesh-refinement code for astrophysics. Astrophys J Suppl Ser 186(2):457

    Article  Google Scholar 

  • Schive H-Y, ZuHone JA, Goldbaum NJ, Turk MJ, Gaspari M, Cheng C-Y (2018) Gamer-2: a GPU-accelerated adaptive mesh refinement code-accuracy, performance, and scalability. Mon Not R Astron Soc 481(4):4815–4840

    Article  Google Scholar 

  • Schroeder W, Martin K, Lorensen B, Inc Kitware (2006) The visualization toolkit: an object-oriented approach to 3D graphics. Kitware

  • Squillacote AH, Ahrens J, Law C, Geveci B, Moreland K, King B (2007) The paraview guide, vol 366. Kitware Clifton Park, NY

  • Wald I, Johnson GP, Amstutz J, Brownlee C, Knoll A, Jeffers J, Günther J, Navrátil P (2016) Ospray-a CPU ray tracing framework for scientific visualization. IEEE Trans Visual Comput Graphics 23(1):931–940

    Article  Google Scholar 

  • Wang Y, Chen W, Zhang J, Dong T, Shan G, Chi X (2011) Efficient volume exploration using the gaussian mixture model. IEEE Trans Visual Comput Gr 17(11):1560–1573

    Article  Google Scholar 

  • Wang K-C, Wei T-H, Shareef N, Shen H-W (2019) Ray-based exploration of large time-varying volume data using per-ray proxy distributions. IEEE Trans Visual Comput Gr 26(11):3299–3313

    Article  Google Scholar 

  • Wang K-C, Lu K, Wei TH, Shareef N, Shen H-W (2017) Statistical visualization and analysis of large data using a value-based spatial distribution. In: 2017 IEEE pacific visualization symposium (PacificVis), pp 161–170. IEEE

  • Wang F, Marshak N, Usher W, Burstedde C, Knoll A, Heister T, Johnson CR (2020) Cpu ray tracing of tree-based adaptive mesh refinement data. In: Computer graphics forum, vol 39, pp 1–12. Wiley Online Library https://doi.org/10.1111/cgf.13958

  • Wang Chaoli, Shen Han-Wei (2005) Hierarchical navigation interface: leveraging multiple coordinated views for level-of-detail multiresolution volume rendering of large scientific data sets. In: Ninth international conference on information visualisation (IV’05), pp 259–267. IEEE

  • Zhou L, Hansen C (2013) Interactive rendering and efficient querying for large multivariate seismic volumes on consumer level pcs. In: 2013 IEEE symposium on large-scale data analysis and visualization (LDAV), pp 117–118. IEEE

  • Zimmer Y, Tepper R, Akselrod S (1996) A two-dimensional extension of minimum cross entropy thresholding for the segmentation of ultrasound images. Ultrasound Med Biol 22(9):1183–1190

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Applied Physics and Computational Mathematics, Beijing, 100094, China

    Yu Wu, Yang Yang & Yi Cao

  2. CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088, China

    Yi Cao

Authors
  1. Yu Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yi Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yi Cao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Y., Yang, Y. & Cao, Y. GPU-based adaptive data reconstruction for large-scale statistical visualization. J Vis 26, 899–915 (2023). https://doi.org/10.1007/s12650-022-00892-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12650-022-00892-1

Keywords

Search

Navigation