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Cut to Fit: Tailoring the Partitioning to the Computation
[article]
2018
arXiv
pre-print
Social Graph Analytics applications are very often built using off-the-shelf analytics frameworks. These, however, are profiled and optimized for the general case and have to perform for all kinds of graphs. This paper investigates how knowledge of the application and the dataset can help optimize performance with minimal effort. We concentrate on the impact of partitioning strategies on the performance of computations on social graphs. We evaluate six graph partitioning algorithms on a set of
arXiv:1804.07747v1
fatcat:fapg4zxm65fjrjzskcazqq7gza