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Effective Gpu Acceleration Of Large Scale, Asynchronous Simulations On Graphs

Author

Listed:
  • DUSTIN ARENDT

    (Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States)

  • YANG CAO

    (Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States)

Abstract

The recent emergence of GPGPU programming has resulted in a number of very efficient, but ultimately ad-hoc implementations of GPU accelerated simulations of complex systems. Because developing applications for the GPU is still a difficult and time consuming task, efficient GPU parallelizations of general purpose modeling frameworks are very useful. The dimer automaton is a stochastic modeling and simulation framework with a good balance of robustness, generality, and simplicity with capacity to model a wide range of phenomena. A major advantage of dimer automata is the ease in which they can be applied to any space that can be represented as a graph. Therefore, we have developed an efficient GPU implementation of dimer automata that runs up to 80 times faster than the serial implementation.

Suggested Citation

  • Dustin Arendt & Yang Cao, 2012. "Effective Gpu Acceleration Of Large Scale, Asynchronous Simulations On Graphs," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 15(08), pages 1-20.
    • Handle: RePEc:wsi:acsxxx:v:15:y:2012:i:08:n:s021952591250035x
    DOI: 10.1142/S021952591250035X
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