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Creative Commons License CC-BY-NC Статья опубликована с лицензией Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0) – Лицензия «С указанием авторства – Некоммерческая».
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Fominykh M., Smorkalov A., Morozov M. Extended Stream Processors Texture Generation Model for 3D Virtual Worlds: Evaluation Results

Аннотация: In this paper, we present an extended stream processors texture generation model for displaying educational content in 3D virtual worlds. The model suggests conducting image-processing tasks on stream processors in order to reduce the load on CPU. The main objective of the paper is to provide the evaluation results of the suggested extended model based on a series of tests. The extension of the model consists of using fixed pipeline features of stream processors. The obtained results of performance evaluation confirm high efficiency and veracity of the generalized mathematical and programming models for image processing. High performance can be explained by specificity problem of generating educational content for virtual words because the source data for the synthesis of images and the data area for the resultant images are in the local memory of stream processors.

Ключевые слова:

3D virtual worlds, image processing, stream processors, educational content, vAcademia, performance evaluation, mathematical model, programming model, performance, image synthesis

Abstract: In this paper, we present an extended stream processors texture generation model for displaying educational content in 3D virtual worlds. The model suggests conducting image-processing tasks on stream processors in order to reduce the load on CPU. The main objective of the paper is to provide the evaluation results of the suggested extended model based on a series of tests. The extension of the model consists of using fixed pipeline features of stream processors. The obtained results of performance evaluation confirm high efficiency and veracity of the generalized mathematical and programming models for image processing. High performance can be explained by specificity problem of generating educational content for virtual words because the source data for the synthesis of images and the data area for the resultant images are in the local memory of stream processors.

Keywords:

3D virtual worlds, image processing, stream processors, educational content, vAcademia, performance evaluation, mathematical model, programming model, performance, image synthesis


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Библиография
1. C. Dede, "Immersive Interfaces for Engagement and Learning," Science, vol. 323(5910), 2009, pp. 66–69, doi:10.1126/science.1167311.
2. R. Mckerlich, M. Riis, T. Anderson, and B. Eastman, "Student Perceptions of Teaching Presence, Social Presence, and Cognitive Presence in a Virtual World," Journal of Online Learning and Teaching, vol. 7(3), 2011, pp. 324–336.
3. R. Marroquim and A. Maximo, "Introduction to GPU Programming with GLSL," in Tutorials of the XXII Brazilian Symposium on Computer Graphics and Image Processing, 2009, pp. 3-16, doi:10.1109/SIBGRAPI-Tutorials.2009.9.
4. K. Fatahalian and M. Houston, "A closer look at GPUs," Communications of the ACM, vol. 51(10), October 2008 2008, pp. 50–57, doi:10.1145/1400181.1400197.
5. D. B. Kirk and W.-m. W. Hwu, Programming Massively Parallel Processors: A Hands-on Approach. New York, USA: Morgan Kaufmann, 2012.
6. K. Fatahalian, "From Shader Code to a Teraflop: How a Shader Core Works," in Beyond Programmable Shading Course New York, NY, USA: ACM SIGGRAPH, 2010.
7. OpenGL machine, http://www.opengl.org/documentation/specs/version1.1/state.pdf.
8. O. Harrison and J. Waldron, "Optimising data movement rates for parallel processing applications on graphics processors," in Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: parallel and distributed computing and networks, Innsbruck, Austria, 2007, pp. 251–256.
9. A. Pooley, A. S. Christensen, B. Merry, D. Garcia, E. Werness, G. Kolling, G. Roth, J. Green, J. Bolz, J. Sandmel, J. Blankenship, J. Leech, M. Callow, P. Brown, R. Simpson, and T. Olson, OpenGL extension ARB_get_program_binary specification, 2010, http://www.opengl.org/registry/specs/ARB/get_program_binary.txt.
10. A. Smorkalov, M. Fominykh, and M. Morozov, "Stream Processors Texture Generation Model for 3D Virtual Worlds: Learning Tools in vAcademia," in 9th International Symposium on Multimedia (ISM), Anaheim, CA, USA, 2013, pp. 17–24, doi:17 10.1109/ISM.2013.13.
11. A. Smorkalov, M. Fominykh, and M. Morozov, "Collaborative Work and Learning with Large Amount of Graphical Content in a 3D Virtual World Using Texture Generation Model Built on Stream Processors," International Journal of Multimedia Data Engineering and Management (IJMDEM), vol. 5(2), 2014, pp. 18–40, doi:10.4018/ijmdem.2014040102.
12. M. Morozov, A. Gerasimov, M. Fominykh, and A. Smorkalov, "Asynchronous Immersive Classes in a 3D Virtual World: Extended Description of vAcademia," in Transactions on Computational Science XVIII. vol. 7848, M. Gavrilova, C. J. K. Tan, and A. Kuijper, Eds.: Springer Berlin Heidelberg, 2013, pp. 81–100, doi:10.1007/978-3-642-38803-3_5.
References
1. C. Dede, "Immersive Interfaces for Engagement and Learning," Science, vol. 323(5910), 2009, pp. 66–69, doi:10.1126/science.1167311.
2. R. Mckerlich, M. Riis, T. Anderson, and B. Eastman, "Student Perceptions of Teaching Presence, Social Presence, and Cognitive Presence in a Virtual World," Journal of Online Learning and Teaching, vol. 7(3), 2011, pp. 324–336.
3. R. Marroquim and A. Maximo, "Introduction to GPU Programming with GLSL," in Tutorials of the XXII Brazilian Symposium on Computer Graphics and Image Processing, 2009, pp. 3-16, doi:10.1109/SIBGRAPI-Tutorials.2009.9.
4. K. Fatahalian and M. Houston, "A closer look at GPUs," Communications of the ACM, vol. 51(10), October 2008 2008, pp. 50–57, doi:10.1145/1400181.1400197.
5. D. B. Kirk and W.-m. W. Hwu, Programming Massively Parallel Processors: A Hands-on Approach. New York, USA: Morgan Kaufmann, 2012.
6. K. Fatahalian, "From Shader Code to a Teraflop: How a Shader Core Works," in Beyond Programmable Shading Course New York, NY, USA: ACM SIGGRAPH, 2010.
7. OpenGL machine, http://www.opengl.org/documentation/specs/version1.1/state.pdf.
8. O. Harrison and J. Waldron, "Optimising data movement rates for parallel processing applications on graphics processors," in Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: parallel and distributed computing and networks, Innsbruck, Austria, 2007, pp. 251–256.
9. A. Pooley, A. S. Christensen, B. Merry, D. Garcia, E. Werness, G. Kolling, G. Roth, J. Green, J. Bolz, J. Sandmel, J. Blankenship, J. Leech, M. Callow, P. Brown, R. Simpson, and T. Olson, OpenGL extension ARB_get_program_binary specification, 2010, http://www.opengl.org/registry/specs/ARB/get_program_binary.txt.
10. A. Smorkalov, M. Fominykh, and M. Morozov, "Stream Processors Texture Generation Model for 3D Virtual Worlds: Learning Tools in vAcademia," in 9th International Symposium on Multimedia (ISM), Anaheim, CA, USA, 2013, pp. 17–24, doi:17 10.1109/ISM.2013.13.
11. A. Smorkalov, M. Fominykh, and M. Morozov, "Collaborative Work and Learning with Large Amount of Graphical Content in a 3D Virtual World Using Texture Generation Model Built on Stream Processors," International Journal of Multimedia Data Engineering and Management (IJMDEM), vol. 5(2), 2014, pp. 18–40, doi:10.4018/ijmdem.2014040102.
12. M. Morozov, A. Gerasimov, M. Fominykh, and A. Smorkalov, "Asynchronous Immersive Classes in a 3D Virtual World: Extended Description of vAcademia," in Transactions on Computational Science XVIII. vol. 7848, M. Gavrilova, C. J. K. Tan, and A. Kuijper, Eds.: Springer Berlin Heidelberg, 2013, pp. 81–100, doi:10.1007/978-3-642-38803-3_5.
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