Abstract
The Breadth-First Search (BFS) algorithm is an important building block for graph analysis of large datasets. The BFS parallelisation has been shown to be challenging because of its inherent characteristics, including irregular memory access patterns, data dependencies and workload imbalance, that limit its scalability. We investigate the optimisation and vectorisation of the hybrid BFS (a combination of top-down
and bottom-up approaches for BFS) on the Xeon Phi, which has advanced vector processing capabilities. The results show that our new implementation improves by 33%, for a one million vertices graph, compared to the state-of-the-art.
and bottom-up approaches for BFS) on the Xeon Phi, which has advanced vector processing capabilities. The results show that our new implementation improves by 33%, for a one million vertices graph, compared to the state-of-the-art.
| Original language | English |
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| Title of host publication | Proceedings of the ACM International Conference on Computing Frontiers |
| Publisher | Association for Computing Machinery |
| ISBN (Print) | 978-1-4503-4487-6 |
| DOIs | |
| Publication status | Published - 1 May 2017 |