Accelerating clustering coefficient calculations on a GPU using OPENCL

Abstract

The growth in multicore CPUs and the emergence of powerful manycore GPUs has led to proliferation of parallel applications. Many applications are not straight forward to be parallelized. This paper examines the performance of a parallelized implementation for calculating measurements of Complex Networks. We present an algorithm for calculating complex networks topological feature clustering coefficient, and conducted an execution of the serial, parallel and parallel GPU implementations. A hash-table based structure was used for encoding the complex network's data, which is different than the standard representation, and also speedups the parallel GPU implementations. Our results demonstrate that the parallelization of the sequential implementations on a multicore CPU, using OpenMP produces a significant speedup. Using OpenCL on a GPU produces even larger speedup depending of the volume of data being processed.