12417

Parallelizing the cellular potts model on GPU and multi-core CPU: An OpenCL cross-platform study

Chao Yu, Bo Yang
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China
11th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2014

@inproceedings{yu2014parallelizing,

   title={Parallelizing the cellular potts model on GPU and multi-core CPU: An OpenCL cross-platform study},

   author={Yu, Chao and Yang, Bo},

   booktitle={Computer Science and Software Engineering (JCSSE), 2014 11th International Joint Conference on},

   pages={117–122},

   year={2014},

   organization={IEEE}

}

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In this paper, we present the analysis and development of a cross-platform OpenCL parallelization of the Cellular Potts Model (CPM). In general, the evolution of the CPM is time-consuming. Using data-parallel programming model such as CUDA can accelerate the process, but it is highly dependent on the hardware type and manufacturer. Recently, OpenCL has attracted a lot of attention and been widely used by researchers. OpenCL provides a flexible solution, which allows us to come up with an implementation that can execute on both GPUs and multi-core CPUs regardless of the hardware type and manufacturer. Some optimizations are also made for both GPU and multi-core CPU implementations of the CPM, and we also propose a resource management method, MLBBRM. Experimental results show that the developed optimized algorithms for both GPU and multi-core CPU have an average speedup of about 30x and 8x respectively compared with the single threaded CPU implementation.
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