Surendra Kumar Shukla

Work place: Dept. of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

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Research Interests: Image Compression, Image Manipulation, Information Security, Parallel Computing, Image Processing, Medical Image Computing

Biography

Dr. Surendra Kumar Shukla is currently working as an Associate Professor, at Graphic era Deemed to be University, Dehradun. He completed his B.E in Computer Engineering from SGSITS College Indore, India in 2004, M.E in Computer Engineering from IET, Devi Ahilya University, Indore, India in 2011. And Ph.D. from DAVV Indore, India in 2021. He has worked with different engineering universities in a span of 15 years in the Department of Computer Engineering. His research area is Multi-core architectures, Parallel Computing, Biomedical image processing and information security

Author Articles
A Theoretical Graph based Framework for Parameter Tuning of Multi-core Systems

By Surendra Kumar Shukla Devesh Pratap Singh Shaili Gupta Kireet Joshi Vishan Kumar Gupta

DOI: https://doi.org/10.5815/ijwmt.2022.04.02, Pub. Date: 8 Aug. 2022

Multi-core systems are outperforming nowadays. Therefore, various computing paradigms are intrinsically incorporated in the multicore domain to exploit its potential and solve well known computing problems. Parameter tuning is a well-known computing problem in the field of Multicore domain. Addressing the said hurdle would leverage in the performance enhancement of Multicore systems. Various efforts in this direction have been made through the conventional parameter tuning algorithms in a limited scope; however, the problem is yet not addressed completely. In this research article, we have addressed parameter tuning problem by employing applications of graph theory, especially Dijkstra shortest path algorithm to address the said issue. Dijkstra’s principle has been applied to establish correlation among the parameters further tuning by finding the pair of suitable parameters. Two other algorithms which are based on application feedback (to provide performance goals to the system) has been introduced. The proposed algorithms collectively (as a framework), addressed the parameter tuning problem. The effectiveness of the algorithms is verified and further measured in distinct parameter tuning scenarios and promising outcome has been achieved.

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