CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Parallel Implementation of Scheduling Algorithms on GPU using CUDA

by Nipun Agarwal, Aman Goyal, Gaurav Maheshwari, and Alok Dugtal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 127 - Number 2
Year of Publication: 2015
Authors: Nipun Agarwal, Aman Goyal, Gaurav Maheshwari, and Alok Dugtal
10.5120/ijca2015906339

Nipun Agarwal, Aman Goyal, Gaurav Maheshwari, and Alok Dugtal . Parallel Implementation of Scheduling Algorithms on GPU using CUDA. International Journal of Computer Applications. 127, 2 ( October 2015), 44-49. DOI=10.5120/ijca2015906339

@article{ 10.5120/ijca2015906339,
author = { Nipun Agarwal, Aman Goyal, Gaurav Maheshwari, and Alok Dugtal },
title = { Parallel Implementation of Scheduling Algorithms on GPU using CUDA },
journal = { International Journal of Computer Applications },
issue_date = { October 2015 },
volume = { 127 },
number = { 2 },
month = { October },
year = { 2015 },
issn = { 0975-8887 },
pages = { 44-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume127/number2/22705-2015906339/ },
doi = { 10.5120/ijca2015906339 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:19:21.915887+05:30
%A Nipun Agarwal
%A Aman Goyal
%A Gaurav Maheshwari
%A and Alok Dugtal
%T Parallel Implementation of Scheduling Algorithms on GPU using CUDA
%J International Journal of Computer Applications
%@ 0975-8887
%V 127
%N 2
%P 44-49
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The future of computation is the GPU, i.e. the Graphical Processing Unit. The graphics cards have shown the tremendous power in the field of image processing and accelerated generating of 3D scenes, and the computational capability of GPUs have promised its developing into great parallel computing units. It is quite simple to program a graphical processor to perform many parallel tasks. But after understanding the various aspects of the graphical processor, it can be used to perform other useful tasks as well. This paper shows how CUDA can fully utilize the tremendous power of these GPUs. CUDA is NVIDIA’s parallel computing architecture which enables terrible increase in computing performance, by gearing the power of the GPU. In the first phase, several operating system algorithms in single threaded CPU environment are implemented using C language, then the same algorithms are implemented on CUDA and CUDA enabled GPU in a parallel environment and finally comparison of their performance and results to their implementation in GPU and CPU are shown.

References
  1. Shuai C., Michael B., Jiayuan M., David T., Jeremy W. S., Kevin S., Performance Study of General-Purpose Applications on Graphics Processors Using CUDA
  2. Maria Andreina F. Rodriguez, “CUDA: Speeding Up Parallel Computing”.
  3. Wikipedia- “http://en.wikipedia.org/wiki/CUDA”
  4. Anthony Lippert – “NVIDIA GPU Architecture for General Purpose Computing”
  5. David Kirk/NVIDIA and Wen-mei Hwu, 2006-2008 – “CUDA Threads”
  6. Yadav K., Mittal A., Ansari M. A., Vishwarup V., “Parallel Implementation of Similarity Measures on GPU Architecture using CUDA”
  7. Direct Compute Programming Guide (http://developer.download.NVIDIA.com/compute/DevZone/docs/html/DirectCompute/doc/DirectCompute_Programming_Guide.pdf)
  8. Singh B.M., Mittal A., Ghosh D., Parallel Implementation of Niblack’s Binarization Approach on CUDA.
  9. Peter Zalutaski “CUDA – Supercomputing for masses.”
  10. Practical Applications for CUDA (http://supercomputingblog.com/cuda/practical-applicationsfor-cuda/)
  11. Matthew Guidry, Charles McClendon, “Parallel Programming with CUDA”.
  12. NVIDIA Corporation. NVIDIA CUDA Compute Unified Device Architecture Programming Guide, June 2008.
  13. Danilo De Donno et al., “Introduction to GPU Computing and CUDA Programming: A Case Study on FDTD,” IEEE Antennas and Propagation Magazine, June 2010.
  14. Practical Applications for CUDA http://supercomputingblog.com/cuda/practical-applications-for-cuda
  15. GPU Gems 2, Chapter 35. GPU Program Optimization http://http.developer.NVIDIA.com/GPUGems2/gpu gems_chapter35.html
  16. Process Scheduling. Available online: https://www.cs.rutgers.edu/~pxk/416/notes/07-scheduling.html 2003-2015.
  17. CPU Scheduling. Available online: http://www.cs.uic.edu/~jbell/CourseNotes/OperatingSystems/5_CPU_Scheduling.html
  18. Types of Scheduling. Available online: http://www.go4expert.com/articles/types-of-scheduling-t22307/
  19. Alexandra Fedorova. “Operating System Scheduling for Chip Multithreaded Processors”, https://www.cs.sfu.ca/~fedorova/thesis
  20. Daniel Alexander Taranovsky, CPU “Scheduling in Multimedia Operating Systems”,1999
  21. David Tarditi, Sidd Puri, Jose Oglesby, “Accelerator: Using Data Parallelism to Program GPUs for General-Purpose Uses”, October 2006
  22. Shuai Che, Michael Boyer, Jiayuan Meng, David Tarjan, Jeremy W. Sheaffer, Kevin Skadron, “A Performance Study of General-Purpose Applications on Graphics Processors Using-CUDA”
  23. Manish Arora, “The Architecture and Evolution of CPU-GPU Systems for General Purpose-Computing“.
  24. Jayshree Ghorpade , Jitendra Parande , Madhura Kulkarni , Amit Bawaskar, “GPGPU PROCESSING IN CUDA ARCHITECTURE” Advanced Computing: An International Journal ( ACIJ ), Vol.3, No.1, January 2012
Index Terms

Computer Science
Information Sciences

Keywords

CUDA Scheduling Algorithms FCFS SJF RR PBS

Powered by PhDFocusTM