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The problem of computing the Betweenness Centrality (BC) is important inanalyzing graphs in many practical applications like social networks,biological networks, transportation networks, electrical circuits, etc. Sincethis problem is computation intensive, researchers have been developingalgorithms using high performance computing resources like supercomputers,clusters, and Graphics Processing Units (GPUs). Current GPU algorithms forcomputing BC employ Brandes' sequential algorithm with different trade-offs forthread scheduling, data structures, and atomic operations. In this paper, westudy three GPU algorithms for computing BC of unweighted, directed, scale-freenetworks. We discuss and measure the trade-offs of their design choices aboutbalanced thread scheduling, atomic operations, synchronizations and latencyhiding. Our program is written in NVIDIA CUDA C and was tested on an NVIDIATesla M2050 GPU.

A (Somewhat Dated) Comparative Study of Betweenness Centrality Algorithms on GPU