Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.723124
Title: Developing graph-based co-scheduling algorithms with GPU acceleration
Author: Zhu, Huanzhou
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2016
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Abstract:
On-chip cache is often shared between processes that run concurrently on different cores of the same processor. Resource contention of this type causes the performance degradation to the co-running processes. Contention-aware co-scheduling refers to the class of scheduling techniques to reduce the performance degradation. Most existing contention-aware co-schedulers only consider serial jobs. However, there often exist both parallel and serial jobs in computing systems. This thesis aims to tackle these issues. We start with modelling the problem of co-scheduling the mix of serial and parallel jobs as an Integer Programming (IP) problem. Then we construct a co-scheduling graph to model the problem, and a set of algorithms are developed to find both optimal and near-optimal solutions. The results show that the proposed algorithms can find the optimal co-scheduling solution and that the proposed approximation technique is able to find the near optimal solutions. In order to improve the scalability of the algorithms, we use GPU to accelerate the solving process. A graph processing framework, called WolfPath, is proposed in this thesis. By taking advantage of the co-scheduling graph, WolfPath achieves significant performance improvement. Due to the long preprocessing time of WolfPath, we developed WolfGraph, a GPU-based graph processing framework that features minimal preprocessing time and uses the hard disk as a memory extension to solve large-scale graphs on a single machine equipped with a GPU device. Comparing with existing GPU-based graph processing frameworks, WolfGraph can achieve similar execution time but with minimal preprocessing time.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.723124  DOI: Not available
Keywords: QA76 Electronic computers. Computer science. Computer software
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