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Reseach Article

A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program

by Chaker El Amrani
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 20
Year of Publication: 2014
Authors: Chaker El Amrani

Chaker El Amrani . A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program. International Journal of Computer Applications. 107, 20 ( December 2014), 28-30. DOI=10.5120/18870-0460

@article{ 10.5120/18870-0460,
author = { Chaker El Amrani },
title = { A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 20 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 28-30 },
numpages = {9},
url = { },
doi = { 10.5120/18870-0460 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:41:36.660774+05:30
%A Chaker El Amrani
%T A Learning Approach to Introducing GPU Computing in Undergraduate Engineering Program
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 20
%P 28-30
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

The graphics processing unit (GPU) learning initiative is developed within a project awarded by the Moroccan Fulbright Alumni Association (MFAA), entitled "GPU Acceleration of Human Genome Sequencing". This project involves undergraduate students at Abdelmalek Essaadi University, and is conducted in collaboration with the High Performance Computing Lab (HPCL) at the George Washington University in U. S. The study brings together two of the most important topics and challenges for the medical field, Genomics, and information technology, parallel computing specially with Graphical Processing Units. The potential outcomes from the project will make very valuable contributions to medical and information technology research and will enrich the academic experience of the students.

  1. Wen-mei W. Hwu, "GPU Computing Gems Emerald Edition (Applications of GPU Computing Series)", Morgan Kaufmann Publishers, 2011.
  2. F. Zheng, X. Xu, Y. Yang, S. He, Y. Zhang, "Accelerating biological sequence alignment algorithm on GPU with CUDA", Proc. International Conference on Computational and Information Sciences (ICCIS 2011), 2011, pp. 18-21.
  3. National Institutes of Health website : http://www. ncbi. nlm. nih. gov/
  4. NVIDIA website: http://www. nvidia. com/object/cuda_home_new. html
  5. D. Kirk and Wen-Mei W. Hwu, "Programming Massively Parallel Processors: A Hands-on Approach", Morgan Kaufmann Publishers, 2010.
  6. J. Sanders, E. Kandrot, "CUDA by Example: An Introduction to General-Purpose GPU Programming", Addison-Wesley Professional, 2010.
  7. R. Farber, "CUDA Application Design and Development", Morgan Kaufmann Publishers, 2011.
  8. C. P. Gribble, "Introducing multithreaded programming: POSIX threads and NVIDIA's Cuda", Computers in Education Journal 19 (4), pp. 104-112, 2009.
  9. A. Kayi, T. El-Ghazawi, and G. Newby: "Performance Issues in Emerging Homogeneous Multicore Architectures", Advances in System Performance Modeling, Analysis, and Enhancement. Elsevier Journal: Simulation, Modeling Practice and Theory, Vol 17, Issue 9, pp. 1485-1499, October 2009.
  10. F. Feldhaus, S. Freitag and C. El Amrani, " State-of-the-Art Technologies for Large-Scale Computing", Ch. 1, pp. 1-17, in Werner Dubitzky, Krzysztof Kurowski and Bernhard Schott, Large-Scale Computing Techniques for Complex System Simulations, Wiley-IEEE Computer Society Pr, 2011.
  11. K. Sharma, A. Saxena, P. Kumar, "Alignment of DNA sequence using the features of global and local algorithms along with matrices", Advanced Materials Research, Volume 403-408, 2012, Pages 2012-2015.
  12. E. Rucci, A. D Giusti, F. Chichizola, M. Naiouf, L. D. Giusti, "DNA sequence alignment: Hybrid parallel programming on a multicore cluster", Recent Advances in Computers, Communications, Applied Social Science and Mathematics, Proc. of ICANCM'11, ICDCC'11, IC-ASSSE-DC'11 , pp. 183-190.
  13. Project website: http://www. fstt. ac. ma/ginfo/gpu-programming/
  14. D. Díaz, F. J. Esteban, P. Hernández, J. A. Caballero, G. Dorado, S. Gálvez, "Parallelizing and optimizing a bioinformatics pairwise sequence alignment algorithm for many-core architecture", Parallel Computing, Vol 37, Issue 4-5, pp. 244-259, April 2011.
  15. M. Bailey, S. Cunningham, "A hands-on environment for teaching GPU programming", SIGCSE 2007: 38th SIGCSE Technical Symposium on Computer Science Education , pp. 254-258. IBM Cloud Computing, Academic Initiative program website: https://www. ibm. com/developerworks/university/cloud/
Index Terms

Computer Science
Information Sciences


High Performance Computing GPU CUDA programming learning-by-doing sequences alignment algorithms bioinformatics.