Kireet Joshi

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

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Research Interests: Interaction Design, Computational Learning Theory, Algorithm Design

Biography

Mr. Kireet Joshi has received his B.Tech in Computer Science & Engineering in Honors from H.N.B Garhwal University, Uttarakhand, and M.Tech in Computer Science & Engineering in Honors. from Govt. Kumaon Engineering College, Dwarahat, Uttarakhand. He has more than 9 years of professional experience. His research interests include Algorithm Design, Machine Learning. Currently he is working as an Assistant Professor, at Graphic Era Deemed to be University, Dehradun, India.

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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