Internal and External Analysis Considering the Layers of Three-dimensional Shapes Using CUDA
 |
International Journal of Computer Trends and Technology (IJCTT) |  |
| © 2019 by IJCTT Journal | ||
| Volume-67 Issue-6 |
||
| Year of Publication : 2019 | ||
| Authors : Satoshi Kodama, Yuka Ozeki, Rei Nakagawa | ||
| DOI : 10.14445/22312803/IJCTT-V67I6P101 |
MLA Style:Satoshi Kodama, Yuka Ozeki, Rei Nakagawa"Internal and External Analysis Considering the Layers of Three-dimensional Shapes Using CUDA" International Journal of Computer Trends and Technology 67.6 (2019): 1-10.
APA Style:Satoshi Kodama, Yuka Ozeki, Rei Nakagawa (2019). Internal and External Analysis Considering the Layers of Three-dimensional Shapes Using CUDA International Journal of Computer Trends and Technology, 67(6), 1-10.
Abstract
With the development of three-dimensional (3D) printing, virtual reality (VR), and augmented reality (AR), a method to accurately determine the structures of 3D objects in various fields including computer-aided design (CAD) is required. However, unlike two-dimensional structures, analyzing 3D structures is highly problematic in terms of processing speed because the involvement of large number of data. In general, although it is possible to formulate an algorithm based on contact determination to achieve drawing at high speed, it is impossible with such an approach to capture the shape of the structure itself and therefore it cannot be used directly in CAD or 3D printing. Herein, to capture the structure, stereoscopic angles are used to perform internal and external determination for any point in three dimensions. However, the algorithm using the solid angle can be accurately internal and external determination, whereas a method using a conventional central processing unit with a simple triangular function is very problematic in terms of speed. From the above, to focus on the effectiveness of the parallel arithmetic when performing the internal and external determination in accordance with the algorithm using the stereoscopic angle, this study proposes a high-speed determination method using general-purpose graphics processing unit (GPGPU). In addition, by extending the idea of the 3D angle, it is shown that the shape can be captured accurately and quickly even for a complex shape (a 3D object including layering) inside another complex shape.
Reference
[1] THE NUNATAK GROUP, VIRTUAL & AUGMENTED REALITY, UPDATE, 4(7), 2016.
[2] Dusty Robbins, Chris Cholas, Mike Brennan, Keith Critchley, Augmented and Virtual Reality for Service Providers, Intel Corporation, Immersive media Business Brief, 2017.
[3] P. Jimenez, F. Thomas, C. Torras, 3D collision detection: a survey, Computers & Graphics, 25(2), 269-285, 2001.
[4] Y. Ozeki, S. Kameyama, S. Kodama, S. Akashi. A Proposal for the User Interface by Using Laser Devices Arranged in a Three Dimensional Space, Proceedings of IPSJ/IEICE Forum on Information Technology, Vol. 3, 385-388, 2016.
[5] Nakayama Atsushi, Kawakatsu Daisuke, Kobori Ken-ichi, Kutsuwa Toshirou, A Checking Method for a Point Inside a Polyhedron in Grasping an Object of VR, Proceedings of the 48th National Convention of IPSJ, 2, 297–298, 1994.
[6] S. Kodama, Verification of Efficacy of Inside-Outside Judgement in Respect of a 3D-Primitive Shapes Using GPGPU, International Journal of Modern Research in Engineering and Technology, 2(3), 1-11, 2017.
[7] Dan Sunday, Inclusion of a Point in a Polygon, http:// geomalgorithms.com/a03-_inclusion.html.
[8] S. Kodama, Effectiveness of inside/outside determination in relation to 3D non-convex shapes using CUDA, The Imaging Science Journal, DOI: 10.1080/13682199.2018. 1497251, 2018.
[9] Adrian Kaehler, Gary Bradski, Learning OpenCV 3: Computer Vision in C++ with the OpenCV Library, O`Reilly Media, 978-1491937990, 2017.
[10] Samuel D. Jaffee, Laura Marie Leventhal, Jordan Ringenberg, G. Michael Poor, Interactive 3D Objects, Projections, and Touchscreens, Proceedings of the Technology, Mind, and Society, DOI: 10.1145/3183654. 3183669, 2018.
[11] Andrew Davison, Pro Java 6 3D Game Development: Java 3D, JOGL, JInput and JOAL APIs, Apress, 978-W1590598177, 2007.
[12] Richard G. Baldwin, Understanding Transforms in Java (Java Programming Notes # 1552), https://www. developer.com/java/other/article.php/3717101/Understanding-Transforms-in-Java-3D.htm, 2007.
[13] John Cheng, Max Grossman, Ty McKercher, Professional CUDA C Programming, Wrox Press Ltd., 9781118739327, 2014.
[14] David B. Kirk, Wen-mei W. Hwu, Programming Massively Parallel Processors, A Hands-on Approach 3rd Edition, Morgan Kaufmann, 9780128119860, 2016.
[15] Jason Sanders, Edward Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Programming, Addison-Wesley Professional, 978-0131387683, 2010.
[16] Duane Storti, Mete Yurtoglu, CUDA for Engineers: An Introduction to High-Performance Parallel Computing, Addison-Wesley Professional, 978-0134177410, 2015.
[17] John Nickolls, GPU parallel computing architecture and CUDA programming model, IEEE Hot Chips 19 Symposium, DOI: 10.1109/HOTCHIPS.2007.7482491, 2007.
[18] Onur Mutlu, Computer Architecture: SIMD and GPUs (Part III) (and briefly VLIW, DAE, Systolic Arrays), Carnegie Mellon University, https://www.archive.ece.cmu.edu/~ece 740/f13/lib/exe/fetch.php?media=onur-740-fall13-module5. 1.3-simd-and-gpus-part3-vliw-dae-systolic.pdf.
[19] John Nickolls, William J. Dally, The GPU Computing Era, IEEE Micro, 30(2), 56 - 69, DOI: 10.1109/MM.2010.41, 2010.
[20] Jem Davies, The bifrost GPU architecture and the ARM Mali-G71 GPU, IEEE Hot Chips 28 Symposium (HCS), DOI: 10.1109/HOTCHIPS.2016.7936201, 2016.
[21] Avneesh Bhatnagar, Evan Speight, Dan Crawl, Joseph Dunn, John Bennett, Application management techniques for the Bifrost system, Proceedings of the 5th IEEE Workshop on Mobile Computing Systems & Applications (WMCSA 2003), DOI: 10.1109/MCSA.2003.1240768, 66-76, 2003.
[22] S.J. Pennycook, G.R. Mudalige, S.D. Hammond, S.A. Jarvis, Parallelising Wavefront Applications on General-Purpose GPU Devices, Proceedings of the 26th UK Performance Engineering Workshop 2010, ISBN 9780955970320, 111-118, 2010.
[23] Daichi Mukunoki, Toshiyuki Imamura, Daisuke Takahashi, Automatic Thread-Block Size Adjustment for Memory-Bound BLAS Kernels on GPUs, IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSOC), DOI: 10.1109/MCSoC.2016.32, 2016.
[24] Zahid Ansari, Asif Afzal, Moomin Muhiuddeen, Sudarshan Nayak, Literature Survey for the Comparative Study of Various High Performance Computing Techniques, International Journal of Computer Trends and Technology, 27(2), 80-86, 2015.
Keywords
CUDA, Parallel Computing, GPGPU, Internal and External Analysis.