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Agyekum, B, Kissi, E, Yamoah Agyemang, D and Badu, E (2018) Examining barriers for the utilization of non-traditional cost estimating models in developing countries. Journal of Engineering, Design and Technology, 16(06), 814–27.

Aouad, R and Amara, I (2018) Influence of the cutting condition on the wear and the surface roughness in the steel AISI 4140 with mixed ceramic and diamond tool. Journal of Engineering, Design and Technology, 16(06), 828–36.

Chen, Y and Yang, Z (2018) Validity and reliability of rating scales for subjective breathing resistance of wearing respirators. Journal of Engineering, Design and Technology, 16(06), 837–49.

De Silva, N, Rathnayake, U and Kulasekera, K (2018) Under-reporting of construction accidents in Sri Lanka. Journal of Engineering, Design and Technology, 16(06), 850–68.

Kulkarni, S, Edwards, D J, Parn, E A, Chapman, C, Aigbavboa, C O and Cornish, R (2018) Evaluation of vehicle lightweighting to reduce greenhouse gas emissions with focus on magnesium substitution. Journal of Engineering, Design and Technology, 16(06), 869–88.

Nawfal, S and Ali, F (2018) The acceleration of 3D graphics transformations based on CUDA. Journal of Engineering, Design and Technology, 16(06), 925–37.

  • Type: Journal Article
  • Keywords: CUDA; GPU; 3D objects; Transformations; Vertices;
  • ISBN/ISSN: 1726-0531
  • URL: https://doi.org/10.1108/JEDT-04-2018-0072
  • Abstract:
    The purpose of this paper is to achieve the acceleration of 3D object transformation using parallel techniques such as multi-core central processing unit (MC CPU) or graphic processing unit (GPU) or even both. 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 graphic systems, acceleration of vertex transform is very much sought for because it requires many matrix operations (need) to be performed in a real time, so the execution time is essential for such processing. Design/methodology/approach In this paper, the acceleration of 3D object transformation is achieved using parallel techniques such as MC CPU or GPU or even both. Multiple geometric transformations are concatenated together at a time in any order in an interactive manner. Findings 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 s to transform 100 million vertices using LabVIEW and 0.096 s using Visual Studio. Other results also showed the significant speed-up compared to CPU, MC CPU and other previous work computations for the same object complexity. Originality/value The high-speed execution of 3D models is essential in many applications such as medical imaging, 3D games and robotics.

Ndokwana, A and Fore, S (2018) Economic assessment of bioethanol production from maize in South Africa. Journal of Engineering, Design and Technology, 16(06), 973–94.

Owusu-Manu, D, Edwards, D J, Kukah, A, Parn, E A, El-Gohary, H and Hosseini, M R (2018) An empirical examination of moral hazards and adverse selection on PPP projects. Journal of Engineering, Design and Technology, 16(06), 910–24.

Romanello, G (2018) Stability analysis of mobile cranes and determination of outriggers loading. Journal of Engineering, Design and Technology, 16(06), 938–58.

Senaratne, S, Mirza, O, Lambrousis, G and Fernandez-Soncini, A (2018) Structural applicability of steel fibre-recycled aggregate concrete in construction. Journal of Engineering, Design and Technology, 16(06), 959–72.

Witmer, A (2018) Contextual engineering assessment using an influence-identification tool. Journal of Engineering, Design and Technology, 16(06), 889–909.