Abstract
Graph analysis is an intrinsic tool embedded in the big data domain. The demand in processing of bigger and bigger graphs requires highly efficient and parallel applications. In this work we explore the possibility of employing the new PCJ library for distributed calculations in Java. We apply the toolbox to sparse matrix matrix multiplications and the k-means clustering problem. We benchmark the strong scaling performance against an equivalent C++/MPI implementation. Our benchmarks found comparable good scaling results for algorithms using mainly local point-to-point communications, and exposed the potential for logarithmic collective operations directly available in the PCJ library. Further more, we also experienced an improvement of development time to solution, as a result of the high level abstractions provided by Java and PCJ.
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Notes
- 1.
This is specially relevant in the one-dimensional domain decomposition used in the SPMM algorithm.
References
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Acknowledgements
The authors wish to thank Piotr Bała and Marek Nowicki for driving the development of the PCJ library and for fruitful discussions and debugging. This work was partial supported by the CHIST-ERA consortium.
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Istrate, R., Barkoutsos, P.K., Dolfi, M., Staar, P.W.J., Bekas, C. (2018). Exploring Graph Analytics with the PCJ Toolbox. In: Wyrzykowski, R., Dongarra, J., Deelman, E., Karczewski, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2017. Lecture Notes in Computer Science(), vol 10778. Springer, Cham. https://doi.org/10.1007/978-3-319-78054-2_29
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