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An Autotuning Engine for the 3D Fast Wavelet Transform on Clusters with Hybrid CPU + GPU Platforms

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Abstract

This work presents an optimization method to run the 3D-fast wavelet transform (3D-FWT) on a CPU + GPU system. The optimization engine detects the different computing components in the system, and executes the appropriate kernel implemented in both CUDA or OpenCL for GPUs, and programmed with pthreads for a CPU. This engine automatically selects parameters such as the block size, the work-group size or the number of threads to reduce the execution time, and sends proportionally different parts of a video sequence to run concurrently in all the computing components of the system. An analysis of the development and optimization of the 3D-FWT for a hybrid cluster of CPU + GPUs is also described. Different parallel programming paradigms (message passing, shared memory and GPU SIMD) are combined to fully exploit the computing capacity of the different computational elements of the cluster, so resulting in an efficient combination of basic codes developed previously for individual components (CPUs or GPUs) and an important reduction of the compression time of long video sequences.

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Acknowledgments

This work was supported by the Spanish MINECO, as well as European Commission FEDER funds, under Grant TIN2012-38341-C04-03. We are grateful to the reviewers for their valuable comments.

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Authors and Affiliations

  1. Computer Engineering Department, University of Murcia, Murcia, Spain

    Gregorio Bernabé & Javier Cuenca

  2. Computer Science and Systems Department, University of Murcia, Murcia, Spain

    Domingo Giménez

Authors
  1. Gregorio Bernabé
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  2. Javier Cuenca
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  3. Domingo Giménez
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Correspondence to Gregorio Bernabé.

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Bernabé, G., Cuenca, J. & Giménez, D. An Autotuning Engine for the 3D Fast Wavelet Transform on Clusters with Hybrid CPU + GPU Platforms. Int J Parallel Prog 43, 1160–1191 (2015). https://doi.org/10.1007/s10766-014-0328-3

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  • DOI: https://doi.org/10.1007/s10766-014-0328-3

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