ABSTRACT
Graph visualization is essential for the analysis of networks and relational data sets. Often, most of the effort is expended on computing sophisticated layouts of the visual representation of the graph. Even though this is increasingly accelerated by use of graphics processing units (GPUs), the rendering is often considered as circumstantial. In this paper, we present a coherent approach to graph visualization that utilizes all features of modern GPUs. We describe specialized data structures and our GPU-centric pipeline for computing and rendering a layout, while enabling steering and interaction. We evaluate technical aspects of our approach as well as its applicability to huge real-world graphs.
- D. Archambault, J. Abello, J. Kennedy, S. Kobourov, K.-L. Ma, S. Miksch, C. Muelder, and A. C. Telea. Temporal Multivariate Networks. In Multivariate Network Visualization, number 8380 in Lecture Notes in Computer Science, pages 151--174. Springer International Publishing, 2014.Google Scholar
- D. Auber. Tulip - A Huge Graph Visualization Framework. In Graph Drawing Software, Mathematics and Visualization, pages 105--126. Springer Berlin Heidelberg, 2004.Google ScholarCross Ref
- M. Bastian, S. Heymann, and M. Jacomy. Gephi: An open source software for exploring and manipulating networks. In Proceedings of the Third International AAAI Conference on Weblogs and Social Media, pages 361--362, 2009.Google Scholar
- G. D. Battista, P. Eades, R. Tamassia, and I. G. Tollis. Graph Drawing: Algorithms for the Visualization of Graphs. Prentice Hall, 1998. Google ScholarDigital Library
- F. C. Bernstein, T. F. Koetzle, G. J. Williams, E. F. Meyer, Jr, M. D. Brice, J. R. Rodgers, O. Kennard, T. Shimanouchi, and M. Tasumi. The Protein Data Bank: a computer-based archival file for macromolecular structures. Journal of molecular biology, 112(3):535--542, 1977.Google Scholar
- W. Cui, H. Zhou, H. Qu, P. C. Wong, and X. Li. Geometry-Based Edge Clustering for Graph Visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6):1277--1284, 2008. Google ScholarDigital Library
- N. Elmqvist, T.-N. Do, H. Goodell, N. Henry, and J. Fekete. ZAME: Interactive Large-Scale Graph Visualization. In 2008 IEEE Pacific Visualization Symposium, pages 215--222, 2008.Google ScholarCross Ref
- O. Ersoy, C. Hurter, F. Paulovich, G. Cantareiro, and A. Telea. Skeleton-Based Edge Bundling for Graph Visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12):2364--2373, 2011. Google ScholarDigital Library
- Y. Frishman and A. Tal. Multi-Level Graph Layout on the GPU. IEEE Transactions on Visualization and Computer Graphics, 13(6):1310--1319, 2007. Google ScholarDigital Library
- Y. Frishman and A. Tal. Uncluttering Graph Layouts Using Anisotropic Diffusion and Mass Transport. IEEE Transactions on Visualization and Computer Graphics, 15(5):777--788, 2009. Google ScholarDigital Library
- T. M. J. Fruchterman and E. M. Reingold. Graph drawing by force-directed placement. Software: Practice and Experience, 21(11):1129--1164, 1991. Google ScholarDigital Library
- E. Gansner, Y. Hu, S. North, and C. Scheidegger. Multilevel agglomerative edge bundling for visualizing large graphs. In 2011 IEEE Pacific Visualization Symposium, pages 187--194, 2011. Google ScholarDigital Library
- E. R. Gansner and Y. Hu. Efficient Node Overlap Removal Using a Proximity Stress Model. In Graph Drawing, number 5417 in Lecture Notes in Computer Science, pages 206--217. Springer Berlin Heidelberg, 2009. Google ScholarDigital Library
- E. R. Gansner, Y. Koren, and S. North. Graph Drawing by Stress Majorization. In Graph Drawing, number 3383 in Lecture Notes in Computer Science, pages 239--250. Springer Berlin Heidelberg, 2005. Google ScholarDigital Library
- A. Godiyal, J. Hoberock, M. Garland, and J. C. Hart. Rapid Multipole Graph Drawing on the GPU. In Graph Drawing, 90--101. Springer Berlin Heidelberg, 2009. Google ScholarDigital Library
- D. Harel and Y. Koren. A fast multi-scale method for drawing large graphs. In Proceedings of the working conference on Advanced visual interfaces, pages 282--285, 2000. Google ScholarDigital Library
- N. Henry, J.-D. Fekete, and M. J. McGuffin. NodeTrix: a Hybrid Visualization of Social Networks. IEEE Transactions on Visualization and Computer Graphics, 13(6):1302--1309, 2007. Google ScholarDigital Library
- D. Holten, P. Isenberg, J. van Wijk, and J. Fekete. An extended evaluation of the readability of tapered, animated, and textured directed-edge representations in node-link graphs. In 2011 IEEE Pacific Visualization Symposium, pages 195--202, 2011. Google ScholarDigital Library
- D. Holten and J. J. van Wijk. Force-Directed Edge Bundling for Graph Visualization. Computer Graphics Forum, 28(3):983--990, 2009. Google ScholarDigital Library
- C. Hurter, O. Ersoy, and A. Telea. Graph Bundling by Kernel Density Estimation. Computer Graphics Forum, 31:865--874, 2012. Google ScholarDigital Library
- S. Ingram, T. Munzner, and M. Olano. Glimmer: Multilevel MDS on the GPU. IEEE Transactions on Visualization and Computer Graphics, 15(2):249--261, 2009. Google ScholarDigital Library
- T. Kamada and S. Kawai. An algorithm for drawing general undirected graphs. Information Processing Letters, 31(1):7--15, 1989. Google ScholarDigital Library
- M. Khoury, Y. Hu, S. Krishnan, and C. Scheidegger. Drawing Large Graphs by Low-Rank Stress Majorization. Computer Graphics Forum, 31(3pt1):975--984, 2012. Google ScholarDigital Library
- Y. Koren, L. Carmel, and D. Harel. ACE: a fast multiscale eigenvectors computation for drawing huge graphs. In IEEE Symposium on Information Visualization, pages 137--144, 2002. Google ScholarDigital Library
- Y. Koren, L. Carmel, and D. Harel. Drawing Huge Graphs by Algebraic Multigrid Optimization. Multiscale Modeling & Simulation, 1(4):645--673, 2003.Google ScholarCross Ref
- J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM Transactions on the Web, 1(1), 2007. Google ScholarDigital Library
- H. C. Purchase, R. F. Cohen, and M. James. Validating Graph Drawing Aesthetics. In Proceedings of the Symposium on Graph Drawing, GD '95, pages 435--446. Springer, 1996. Google ScholarDigital Library
- E. W. Sayers, T. Barrett, D. A. Benson, S. H. Bryant, K. Canese, V. Chetvernin, D. M. Church, M. DiCuccio, R. Edgar, S. Federhen, M. Feolo, L. Y. Geer, W. Helmberg, Y. Kapustin, D. Landsman, D. J. Lipman, T. L. Madden, D. R. Maglott, V. Miller, I. Mizrachi, J. Ostell, K. D. Pruitt, G. D. Schuler, E. Sequeira, S. T. Sherry, M. Shumway, K. Sirotkin, A. Souvorov, G. Starchenko, T. A. Tatusova, L. Wagner, E. Yaschenko, and J. Ye. Database resources of the National Center for Biotechnology Information. Nucleic acids research, 37(Database issue):D5--15, 2009.Google Scholar
- The UniProt Consortium. Activities at the Universal Protein Resource (UniProt). Nucleic Acids Research, 42(D1):D191--D198, 2013.Google Scholar
- U.S. Census Bureau. County-to-county migration flow files.Google Scholar
- C. Ware, H. Purchase, L. Colpoys, and M. McGill. Cognitive Measurements of Graph Aesthetics. Information Visualization, 1(2):103--110, 2002. Google ScholarDigital Library
- M. Zinsmaier, U. Brandes, O. Deussen, and H. Strobelt. Interactive Level-of-Detail Rendering of Large Graphs. IEEE Transactions on Visualization and Computer Graphics, 18(12):2486--2495, 2012. Google ScholarDigital Library
- Consistently GPU-Accelerated Graph Visualization
Recommendations
An Implementation of GPU Accelerated MapReduce: Using Hadoop with OpenCL for Data- and Compute-Intensive Jobs
IJCSS '12: Proceedings of the 2012 International Joint Conference on Service SciencesMapReduce is an efficient distributed computing model for large-scale data processing. However, single-node performance is gradually to be the bottleneck in compute-intensive jobs. This paper presents an approach of MapReduce improvement with GPU ...
GPU Accelerated Wavelet Transform Profilometry
CSO '12: Proceedings of the 2012 Fifth International Joint Conference on Computational Sciences and OptimizationHuge workload and time-consuming of the phase computation based on the Wavelet Transform Profilometry (WTP) so that not meet real-time three-dimensional (3D) measurement needs. Fortunately the pixels which in situ need to be processed and the already ...
GaccO - A GPU-accelerated OLTP DBMS
SIGMOD '22: Proceedings of the 2022 International Conference on Management of DataIn this paper, we present GaccO - a main memory DBMS for GPU-accelerated OLTP. For executing OLTP workloads, GaccO implements a novel scheme that splits the execution across the CPU and the GPU. Using such a co-execution scheme GaccO can thus not only ...
Comments