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Heuristics for Conversion Process of GPU’s Kernels for Multiples Kernels with Concurrent Optimization Divergence

Jose Ritomar Carneiro Torquato, Esteban Walter Gonzalez Clua
Institute of Computing, Federal Fluminense University, Niteroi, RJ, Brazil
The 2014 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’14), 2014

@article{torquato2014heuristics,

   title={Heuristics for Conversion Process of GPU’s Kernels for Multiples Kernels with Concurrent Optimization Divergence},

   author={Torquato, Jos{‘e} Ritomar Carneiro and Clua, Esteban Walter Gonzalez},

   year={2014}

}

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Graphics Processing Units have been created with the objective of accelerating the construction and processing of graphic images. In its historical evolution line, concerned with the large computational capacity inherent, these devices started to be used for general purposes. However, the design of the GPUs don’t work well with divergent algorithms, mainly conditionals and repetitions. In this work we present a strategy for finding the divergence root of the kernels and try to deduce alternative solutions, decomposing them into concurrent kernels. We developed mechanisms for the user in order to easily readapt his code and take advantages of architectures that support concurrent kernels.
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