Abstract
The PageRank algorithm is a widely used linear algebra method with many applications. As graphs with billions or more of nodes become increasingly common, being able to scale this algorithm on modern HPC architectures is of prime importance. While most existing approaches have explored distributed computing to compute an approximation of the PageRank scores, we focus on the numerical computation using the power iteration method. We develop and implement a distributed parallel version of the PageRank. This application is deployed on the supercomputer Fugaku, using up to one thousand compute nodes to assess scalability on random stochastic matrices. These large-scale experiments show that the network-on-chip of the A64FX processor acts as an additional level of computation, in between nodes and cores.
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Notes
- 1.
Code is available at https://github.com/jgurhem/TBSLA/tree/dev_array.
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Vandromme, M., Gurhem, J., Tsuji, M., Petiton, S., Sato, M. (2022). Scaling the PageRank Algorithm for Very Large Graphs on the Fugaku Supercomputer. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13350. Springer, Cham. https://doi.org/10.1007/978-3-031-08751-6_28
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