Open Access   Article Go Back

Performance Evaluation of High Performance Computing Resources and Job Management

Anika Karwal1 , O.P. Gupta2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 142-146, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.142146

Online published on May 31, 2019

Copyright © Anika Karwal, O.P. Gupta . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

XML View
PDF Download

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Anika Karwal, O.P. Gupta, “Performance Evaluation of High Performance Computing Resources and Job Management,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.142-146, 2019.

MLA Style Citation: Anika Karwal, O.P. Gupta "Performance Evaluation of High Performance Computing Resources and Job Management." International Journal of Computer Sciences and Engineering 7.5 (2019): 142-146.

APA Style Citation: Anika Karwal, O.P. Gupta, (2019). Performance Evaluation of High Performance Computing Resources and Job Management. International Journal of Computer Sciences and Engineering, 7(5), 142-146.

BibTex Style Citation:
@article{Karwal_2019,
author = {Anika Karwal, O.P. Gupta},
title = {Performance Evaluation of High Performance Computing Resources and Job Management},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {142-146},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4212},
doi = {https://doi.org/10.26438/ijcse/v7i5.142146}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.142146}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4212
TI - Performance Evaluation of High Performance Computing Resources and Job Management
T2 - International Journal of Computer Sciences and Engineering
AU - Anika Karwal, O.P. Gupta
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 142-146
IS - 5
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
333 268 downloads 165 downloads
  
  
           

Abstract

High Performance Computing (HPC) is a highly emerging concept in the field of computer science and technology. HPC makes the use of parallel computing to solve complex computational problems at a very high speed. Data and compute intensive applications require distinct and different resources, so it becomes utmost important to manage resources and schedule jobs accordingly. HPC is a hard and complex concept to be understood so most of it remains under-utilized. To improve operational functionality and enhance utilization of HPC many systems have been developed. The system used in this research is PBS. Resource management and job scheduling is a major research area in high performance computing. Portable Batch System (PBS) is a scheduling and resource management system. It is used for job accounting and extensible batch job queueing. Three primary goals of PBS are queueing, scheduling and monitoring the jobs. Along these lines, the fundamental objective of this paper is to give novel powerful resource management and job planning and scheduling techniques that is reasonable for all the above purposes and can be coordinated with HPC frameworks.

Key-Words / Index Term

High Performance Computing (HPC), Job Scheduling, Portable Batch System (PBS), Resource Scheduling

References

[1] B. Bode, D.M. Halstead, R. Kendall and L. Zhou, “The Portable Batch Scheduler and the Maui Scheduler on Linux Clusters”, In the Proceedings of 4th Annual Linux Showcase & Conference, Atlanta, 2000.
[2] M. Hovestadt , O. Kao, A. Keller and A. Streit, “Scheduling in HPC resource management systems: queuing vs. planning”, Lecture notes Computer Science, Springer, Berlin Heidelberg, Vol. 2862, pp. 1-20, 2000.
[3] J. Cao, A. Chan, Y. Sun, S.K. Das and M. Guo, “Taxonomy of application scheduling tools for high performance cluster computing”, Cluster Computing, Vol. 9, pp. 355– 371,2006.
[4] C. Engelmann, S.L. Scott, C. Leangsuksun and X. He, “Towards high availability for high-performance computing system services: Accomplishments and limitations”, In the proceedings of High Availability and Performance Workshop, Santa Fe, NM, USA, Oct. 17, 2006.
[5] Gabriel E, Feki S, Benkert K and Resch M M, “Towards performance portability through runtime adaptation for high-performance computing applications”, Concurrency Computation: Practical Experiment, Vol. 22, pp. 2230–46,2010.