Computational Science Technical Note CSTN-117

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GP-GPU and Multi-Core Architectures for Computing Clustering Coefficients of Irregular Graphs

A. Leist, K. A. Hawick and D. P. Playne

Archived November 2010

Abstract

Network science makes heavy use of simulation models and calculations based upon graph-oriented data structures that are intrinsically highly irregular in nature. The key to efficient use of data-parallel and multi-core parallelism on graphical processing units (GPUs) and CPUs is often to optimise the data layout and to exploit distributed memory locality with processing elements. We describe work using hybrid multi-core and many-core devices and architectures for implementing and optimising applications based upon irregular graph and network algorithms.

Keywords: multi-core; accelerators; GPU; CUDA; Cell; data parallelism

Full Document Text: PDF version.

Citation Information: BiBTeX database for CSTN Notes.

BiBTeX reference:

@INPROCEEDINGS{CSTN-117,
  author = {A. Leist and K. A. Hawick and D. P. Playne},
  title = {GP-GPU and Multi-Core Architectures for Computing Clustering Coefficients
	of Irregular Graphs},
  booktitle = {Proc. International Conference on Scientific Computing (CSC'11)},
  year = {18-21 2011},
  number = {CSC2720},
  pages = {3-9},
  address = {Las Vegas, USA},
  month = {18-21 July},
  publisher = {CSREA},
  timestamp = {2011.05.16}
}


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