Abstract
The widespread availability of modern infrastructures able to process large amounts of data and run sophisticated models of complex phenomena, is making simulation-based research a usual technique among the scientific tools. The impact of these techniques is so large, that they have been touted as the new paradigms for scientific discovery: the third, in relation to large-scale simulations, and fourth, in relation to data-intensive computing. In the traditional approach, the results of complex simulations are typically very large data sets that are later mined for knowledge. In a more dynamic approach, the user interacts with the simulation, steering it and visualizing the results in an exploratory way in order to gain knowledge. If this is properly done, it can not only make better use of the available resources, but also produce insight that would not be possible in a static, post-mortem analysis of the results. However, it is not easy to include live visualization and analysis in a workflow that has been designed to fit the available HW&SW infrastructure and to finish with a set of files for off-line study. This traditional process could very well turn into unfeasible if computing continues its way to a future limited by the storage capabilities, thus making impractical the storage for later analysis paradigm typical of today’s simulations. In this cases, having a scientist in the loop, aided by a set of analysis and data reduction techniques, will be necessary to understand the results and produce new science. The purpose of the present paper is to outline the main problems that have to be solved to visualize simulation results with an application to the Human Brain Project. The complexity and needs of present day visualization tools in this domain will be exemplified using RTNeuron, a code to represent neural activity in close to real time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Main memory (not in the accelerators) in the fastest Top500 machines now in production (early 2014) or soon to be released is 0.6 PB for the 27 Pflop/s Titan at ORNL, USA, and 1PB for the expected 54.9 Pflop/s Tianhe-2, to be installed at NUDT, China. An exaflop computer is expected around 2020.
References
Tufte, E.R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire (2001)
McCormick, B.H., et al.: Visualization in Scientific Computing. Comput. Graph. 21(6), 1–14 (1987)
Yu, H., Wang, C., Grout, R.W., Chen, J.H., Ma, K.-L.: In situ visualization for large-scale combustion simulations. IEEE Comput. Graph. Appl. 30(3), 45–57 (2010)
Dongarra, J.: Performance of various computers using standard linear equations software (Linpack Benchmark Report), University of Tennessee Computer Science Technical Report, CS-89-85 (2013)
Fekete, J.D.: Software and hardware infrastructures for visual analytics. Computer 46(7), 22–29 (2013)
Fisher, D., Popov, I., Drucker, S., Schraefel, M.: Trust me, I’m partially right: incremental visualization lets analysts explore large datasets faster. In: CHI 12, pp. 1673–1682 (2012)
Gewaltig, M.-O., Diesmann, M.: NEST (NEural Simulation Tool). Scholarpedia 2(4), 1430 (2007)
Carnevale, N.T., Hines, M.L.: The NEURON Book. Cambridge University Press, Cambridge (2006)
Hines, M.L., Eichner, H., Schürmann, F.: Fully implicit parallel simulation of single neurons. J. Comput. Neurosci. 25(3), 439–448 (2008)
Markram, H.: The blue brain project. Nat. Rev. Neurosci. 7(2), 153–160 (2006)
Druckmann, S., Banitt, Y., Gidon, A.A., Schürmann, F., Henry, M., Segev, I.: A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front. Neurosci. 1(1), 7–18 (2007)
Hay, E., Hill, S., Schürmann, F., Markram, H., Segev, I.: Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Comput. Biol. 7(7), e1002107 (2011)
Hill, S.L., Wang, Y., Riachi, I., Schürmann, F., Markram, H.: Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits. In: Proceedings of the National Academy of Sciences (2012)
Reimann, M.W., Anastassiou, C.A., Perin, R., Hill, S.L., Markram, H., Koch, C.: A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents. Neuron 79(2), 375–390 (2013)
Haber, R.B., McNabb, D.A.: Visualization idioms: a conceptual model for scientific visualization systems. In: Nielson, G.M., Shriver, B., Rosenblum, L.J. (eds.) Visualization in Scientific Computing, pp. 74–93. IEEE Computer Society, Los Alamitos (1990)
Gruen, H., Thibieroz, N.: OIT and indirect illumination using DX11 linked lists. In: Proceedings of the 2010 Game Developer Conference (2010)
Hernando, J.B., Biddiscombe, J., Bohara, B., Eilemann, S., Schürmann, F.: Practical parallel rendering of detailed neuron simulations. In: Eurographics Symposium on Parallel Graphics and Visualization (2013)
Lasserre, S., Hernando, J.B., Schürmann, F., De Miguel Anasagasti, P., Abou-Jaoudé, G., Markram, H.: A neuron membrane mesh representation for visualization of electrophysiological simulations. IEEE Trans. Visualiz. Comput. Graph. 18(2), 214–227 (2012)
Hilbert, M., López, P.: The world’s technological capacity to store, communicate, and compute information. Science 332, 60–65 (2011)
Eilemann, S., Makhinya, M., Pajarola, R.: Equalizer: a scalable parallel rendering framework. IEEE Trans. Visualiz. Comput. Graph. 15, 436–452 (2009)
Robert, O., Burns, D., et al.: OpenSceneGraph (2001–2014). http://www.openscenegraph.org
Hernando, J.B., Pastor, L., Schürmann, F.: Towards real-time visualization of detailed neural tissue models: view frustum culling for parallel rendering. In: BioVis 2012: 2nd IEEE Symposium on biological data visualization (2012)
Bavoil, L., Meyers, L.K.: Order independent transparency with dual depth peeling. Technical report, NVIDIA Corporation (2008)
Molnar, S., Cox, M., Ellsworth, D., Fuchs, H.: A sorting classification of parallel rendering. IEEE Comput. Graph. Appl. 14(4), 23–32 (1994)
Eilemann, S., Pajarola, R.: Direct send compositing for parallel sort-last rendering. In: Eurographics Symposium on Parallel Graphics and Visualization, pp. 29–36 (2007)
Schroeder, W.J., Martin, K., Lorensen, W.E.: The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, 3rd edn. Kitware Inc. (formerly Prentice-Hall), New York (2003)
Dongarra, J., Beckman, P., et al.: The international exascale software roadmap. Int. J. High Perform. Comput. Appl. 25(1), 3–60 (2011)
Kauker, D., Krone, M., Panagiotidis, A., Reina, G., Ertl, T.: Evaluation of per-pixel linked lists for distributed rendering and comparative analysis. Comput. Vis. Sci. 15(3), 111–121 (2012)
Miller, R.B.: Response time in man-computer conversational transactions. In: Proceedings of AFIPS Fall Joint Computer Conference, vol. 33, pp. 267–277 (1968)
DOE ASCAC, Data Subcommittee: Synergistic Challenges in Data-Intensive Science and Exascale Computing (2013)
Acknowledgements
This work has been partially funded by the Spanish Ministry of Education and Science (Cajal Blue Brain project) and the Human Brain Project (FP7-604102-HBP). The authors would also like to thank the Blue Brain Project for the collaboration framework in which this work has been developed.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Hernando, J.B., Duelo, C., Martin, V. (2014). Visualization of Large-Scale Neural Simulations. In: Grandinetti, L., Lippert, T., Petkov, N. (eds) Brain-Inspired Computing. BrainComp 2013. Lecture Notes in Computer Science(), vol 8603. Springer, Cham. https://doi.org/10.1007/978-3-319-12084-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-319-12084-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12083-6
Online ISBN: 978-3-319-12084-3
eBook Packages: Computer ScienceComputer Science (R0)