Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.728202
Title: Exploring vectorisation for parallel breadth-first search on an advanced vector processor
Author: Paredes Lopez, Mireya
ISNI:       0000 0004 6498 7254
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2017
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Abstract:
Modern applications generate a massive amount of data that is challenging to process or analyse. Graph algorithms have emerged as a solution for the analysis of this data because they can represent the entities participating in the generation of large scale datasets in terms of vertices and their relationships in terms of edges. Graph analysis algorithms are used for finding patterns within these relationships, aiming to extract information to be further analysed. The breadth-first search (BFS) is one of the main graph search algorithms used for graph analysis and its optimisation has been widely researched using different parallel computers. However, the BFS parallelisation has been shown to be chal- lenging because of its inherent characteristics, including irregular memory access patterns, data dependencies and workload imbalance, that limit its scalability. This thesis investigates the optimisation of the BFS on the Xeon Phi, which is a modern parallel architecture provided with an advanced vector processor using a self-created development framework integrated with the Graph 500 benchmark. As a result, optimised parallel versions of two high-level algorithms for BFS were created using vectorisation, starting with the conventional top-down BFS algorithm and, building on this, leading to the hybrid BFS algorithm. The best implementations resulted in speedups of 1.37x and 1.33x, for a one million vertices graph, compared to the state-of-the-art, respectively. The hybrid BFS algorithm can be further used by other graph analysis algorithms and the lessons learned from vectorisation can be applied to other algorithms targeting the existing and future models of the Xeon Phi and other advanced vector architectures.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.728202  DOI: Not available
Keywords: graph algorithms ; graph 500 benchmark ; breadth first search ; parallel architecture ; vectorisation ; Xeon Phi
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