International Journal of Information Engineering and Applications  
Manuscript Information
 
 
GPU Acceleration of 3D Object Transformations
International Journal of Information Engineering and Applications
Vol.1 , No. 3, Publication Date: Jun. 8, 2018, Page: 123-131
1346 Views Since June 8, 2018, 536 Downloads Since Jun. 8, 2018
 
 
Authors
 
[1]    

Sura Nawfal Alrawy, Computer Engineering Department, Mosul University, Mosul, Iraq.

[2]    

Fakhrulddin Hamid Ali, Computer Engineering Department, Mosul University, Mosul, Iraq.

 
Abstract
 

Generating 3D animation scenes in computer graphics requires applying a 3D transformation on the vertices of the objects. These transformations consume most of the execution time. Hence, for high-speed graphics systems, acceleration of vertex transform is very much sought for because it requires many matrices operations that to be performed at a real-time, so the execution time is essential for such processing. In this paper, the acceleration of 3D object transformation is achieved using parallel techniques such as Multicore Core Central Processing Unit (MC CPU) or Graphic Processing Unit (GPU) or even both. Multiple geometric transformations are concatenated together at a time in any order with interactive manner. The performance results are presented for a number of 3D objects with paralleled implementations of the affine transform on the NVIDIA GPU series. The maximum execution time was about 0.508 seconds to transform 100 million vertices. Other results also showed the significant speedup compared to (CPU and MC CPU) computations for the same object complexity.


Keywords
 

GPU, 3D Object, Transformation, Vertices


Reference
 
[01]    

Taylor & Francis Group, CRC Press “Practical Algorithms For 3D Computer Graphics” second edition R. Stuart Ferguson, © 2014.

[02]    

%Tomas Akenine-Möller, Eric Haines, and Naty Hoffman, “Real-Time Rendering”, 1045 pages, from A. K. Peters Ltd., 3rd edition, ISBN 987-1-56881-424-7, 2008.

[03]    

%Fakhrulddin Hamid Ali, “Transformation Matrix for 3D computer Graphics Based on FPGA”, Al-Rafidain Engineering Journal, Vol. 20, No. 5, October 2012.

[04]    

%Sahin, Ibrahim. "A 32-bit floating-point module design for 3D graphic transformations". Scientific Research and Essays Journal, Vol. 5, pp 3070-3081, 18 October 2010.

[05]    

%Biswal Pradyut Kumar, Banerjee Swapna. “A parallel approach for affine transform of 3D biomedical images”. International conference on electronics and information engineering (ICEIE), vol. 1. IEEE; 2010. p. 329–32.

[06]    

%Pradyut Kumar Biswal, Pulak Mondal, Swapna Banerjee; “Parallel architecture for accelerating affine transform in high-speed imaging systems” Journal of Real-Time Image Processing. Springer-Verlag 2011.

[07]    

%Mondal P, Biswal PK, Banerjee S “FPGA based accelerated 3D affine transform for real-time image processing applications”. Comput Electr Eng 49 (1): 69 Elsevier, 2016.

[08]    

%Dr. Basma Mohammed Kamal Younis, Ne'am Salim Mohammed Sheet “A Real Time Dynamic 3D Graphics Processor Using FPGA” international Journal for Research and Development in Engineering (IJRDE) www.ijrde.com Vol. 2: Issue. 1, June-July 2013 pp- 1-12.

[09]    

%Zhiyuan Liu, Xuezhang Zhao “Research and Implementation of Image Rotation Based on CUDA” Advanced Materials Research ISSN: 1662-8985, Vol. 216, pp 708-712 Trans Tech Publications, Switzerland, © 2011. www.scientific.net/AMR.216.708.

[10]    

%Hadeel Alshakargy “Execution Speed up of Image Rotation Matrix Using Parallel Technique”, M.Sc. thesis, Computer Engineering University of Mosul, 2016.

[11]    

%Bozhi Liu, Tao Sun, Li Zhou, Jia Wang and Yuanzhi Zhang "Architecture for Vertex Transformation and Triangle Clipping in 3D Graphics", ISSN: 1662-7482, Vols. 462-463, pp 1040-1045 Trans Tech Publications, Switzerland, © 2014.

[12]    

%N. Taghiyev, M. Akcay, “Parallel matrix multiplication for various implementations,” Application of Information and Communication Technologies (AICT), 2013 7th International Conference on, pp. 1-5, 2013. IEEE Conference.

[13]    

%Nicholas Malaya, Shuai Che, Joseph L. Greathouse, Ren´e van Oostrum, and Michael J. Schulte “Accelerating Matrix Processing with GPUs” Published in the Proceedings of the 24th IEEE Symposium on Computer Arithmetic (ARITH 24), July, 2017.

[14]    

%P. Michailidis, K Margaritis., “Performance Models for Matrix Computations on Multicore Processors using OpenMP”. The 11th Intl. Conf. on Parallel and Distributed Computing, Applications and Technologies, 2010.

[15]    

%Nvidia corporation, “CUDA C Programming Guide” PG-02829-001_v9.0 | September 2017, http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html.

[16]    

%GeForce gtx 1050 characteristics https://www.nvidia.com/en-us/geforce/products.html.

[17]    

%3D computer graphics models from http://graphics.stanford.edu/data/3Dscanrep/.html





 
  Join Us
 
  Join as Reviewer
 
  Join Editorial Board
 
share:
 
 
Submission
 
 
Membership