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Academic Report: Developing the Graph-based Methods for Optimizing Job Scheduling on Multicore Computers

Date:2017-08-31Promulgator:Click:

 

Developing the Graph-based Methods for Optimizing Job Scheduling on Multicore Computers

Lecturer: Ligang He

Time: 15:00, Sep. 1st (Friday), 2017

Venue: 401 Lecture Hall, Building of Information, East Campus

About the lecturer: Dr. He is an Associate Professor in the Department of Computer (REF Ranking 2nd), University of Warwick (ranking 6th in The Times and Sunday Times Good University Guide 2016), the UK. Engaged in the research on high performance computing, parallel distributed processing and cloud computing, he has published 90 academic papers at IEEE Transactions on Parallel and Distributed Systems, Journal of Parallel and Distributed Computing, Journal of Computer and System Sciences and other international core journals; and at VLDBIPDPS, ICPP, ICSOC and other important conferences. Dr. He has hosted many research projects sponsored by EPRSC, Leverhulme and other companies. He is also a member of Editorial board or Special Issue Guest Editorial Board for international journals such as CCPE, and conference Chair or Program Committee member for international conferences such as ICPP.

About the lectureIt is common nowadays that multiple cores reside on the same chip and share the on-chip cache. Resource sharing may cause performance degradation of the co-running jobs. Job co-scheduling is a technique that can effectively alleviate the contention. Many co-schedulers have been developed in the literature, but most of them do not aim to find the optimal co-scheduling solution. Being able to determine the optimal solution is critical for evaluating co-scheduling systems. Moreover, most co-schedulers only consider serial jobs. However, there often exist both parallel and serial jobs in many situations. This talk presents our work to tackle these issues. In this work, a graph-based method is developed to find the optimal co-scheduling solution for serial jobs, and then the method is extended to incorporate parallel jobs. A number of optimization measures are also developed to accelerate the solving process. Moreover, a flexible approximation technique is proposed to strike the balance between the solving speed and the solution quality.

 

      School of Information Science and Engineering

                                                   August 30th, 2017

 

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