Abstract
Graph workloads exhibit highly irregular memory access patterns, resulting in poor cache utilization. By modifying the layout of the stored graph prior to processing, cache utilization can be enhanced. Two factors need to be considered for modifying the layout of the graph. First, the nature of computation in vertex-centric algorithms suggests that vertex neighbours are visited in succession throughout processing. Second, the degree distribution of vertices in real-world networks exhibits power-law distribution, implying that a few vertices are responsible for the majority of the connections. As a result, such nodes can be clustered together to improve temporal and spatial locality. In this paper, we propose Community Aware Graph Reordering (CAR), which leverages both these aspects to enhance the performance of graph applications when compared to existing reordering strategies. While previous state-of-the-art reordering techniques with comparable reordering overheads, such as Hub Cluster, DBG, and Sorder, deliver speedup of 9%, 11%, and 17%, respectively, CAR provides a speedup of 20%.
S. Singhania and N. Sharma—Both authors have contributed equally
C. K. Jha—The work was carried out when Chandan Kumar Jha was at CADSL, IIT Bombay.
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Singhania, S., Sharma, N., Venkitaraman, V., Jha, C.K. (2022). CAR: Community Aware Graph Reordering for Efficient Cache Utilization in Graph Analytics. In: Shah, A.P., Dasgupta, S., Darji, A., Tudu, J. (eds) VLSI Design and Test. VDAT 2022. Communications in Computer and Information Science, vol 1687. Springer, Cham. https://doi.org/10.1007/978-3-031-21514-8_37
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