Computational Science Technical Note CSTN-144

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Halo Gathering Scalability for Large Scale Multi-dimensional Sznajd Opinion Models Using Data Parallelism with GPUs

K.A. Hawick and D.P. Playne

Archived December 2011, Updated March 2012

Abstract

The Sznajd model of opinion formation exhibits complex phase transitional and growth behaviour and can be studied with numerical simulations on a number of different network structures. Large system sizes and detailed statistical sampling of the model both require data-parallel computing to accelerate simulation performance. Data structures and computational performance issues are reported for simulations on single and multi-core processing devices. A discussion of optimal data structures for performance on Graphical Processing Units using NVIDIA's Compute Unified Device Architecture (CUDA) is also given. System size and memory layout tradeoffs for different processing devices are also presented.

Keywords: opinion formation model; interdisciplinary simulation; data-parallelism; GPU; CUDA

Full Document Text: PDF version.

Citation Information: BiBTeX database for CSTN Notes.

BiBTeX reference:

@INPROCEEDINGS{CSTN-144,
  author = {K. A. Hawick and D. P. Playne},
  title = {Halo Gathering Scalability for Large Scale Multi-dimensional Sznajd
	Opinion Models Using Data Parallelism with GPUs},
  booktitle = {Proc. Int. Conf. on Parallel and Distributed Processing Techniques
	and Applications (PDPTA'12)},
  year = {2012},
  pages = {95-101},
  address = {Las Vegas, USA},
  month = {16-19 July},
  publisher = {CSREA},
  institution = {Computer Science, Massey University},
  timestamp = {2012.05.03}
}


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