Accelerating clustering coefficient calculations on a GPU using OPENCL

Date Issued
2010-09-12
Author(s)
Djinevski, Leonid
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.
Subjects

Complex Networks, Par...

File(s)
Loading...
Thumbnail Image
Name

Accelerating_Clustering_Coefficient_Calc20161108-15766-1kf0tro-with-cover-page-v2.pdf

Size

228.87 KB

Format

Adobe PDF

Checksum

(MD5):cd7d7851ff0569ba19c33996f234a979