article

Box-counting algorithm on GPU and multi-core CPU: an OpenCL cross-platform study

Authors:

Jesús Jiménez,

Juan Ruiz De MirasAuthors Info & Claims

The Journal of Supercomputing, Volume 65, Issue 3

Pages 1327 - 1352

https://doi.org/10.1007/s11227-013-0885-z

Published: 01 September 2013 Publication History

Abstract

In this paper, we present the analysis and development of a cross-platform OpenCL implementation of the box-counting algorithm, which is one of the most widely-used methods for estimating the Fractal Dimension. The Fractal Dimension is a relevant image analysis method used in several disciplines, but computing it is in general a time consuming process, especially when working with 3D images. Unlike parallel programming models that strictly depend on the hardware type and manufacturer, like CUDA, OpenCL allows us to provide an implementation suitable for execution on both GPUs and multi-core CPUs, whatever the hardware manufacturer. Sorting is a key part of the fast box-counting algorithm and the final speedup is highly conditioned by the efficiency of the sorting algorithm used. Our study reveals that current OpenCL implementations of sorting algorithms are clearly slower when compared with both CUDA for GPU and specific multi-core CPU implementations. Our OpenCL algorithm has been specifically optimized according the type of the target device and the results show an average speedup of up to 7.46 and 4, when executed on the GPU and the multi-core CPU respectively, both compared with the single-threaded (sequential) CPU implementation.

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Cited By

Ruiz de Miras J(2020)Fast differential box-counting algorithm on GPUThe Journal of Supercomputing10.1007/s11227-019-03030-176:1(204-225)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s11227-019-03030-1
Duarte-Sanchez JVelasco-Medina JMoreno P(2018)Hardware Accelerator for the Multifractal Analysis of DNA SequencesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2017.273133915:5(1611-1624)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1109/TCBB.2017.2731339
Jiménez JLópez ACruz JEsteban FNavas JVilloslada PRuiz de Miras J(2014)A Web platform for the interactive visualization and analysis of the 3D fractal dimension of MRI dataJournal of Biomedical Informatics10.1016/j.jbi.2014.05.01151:C(176-190)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.jbi.2014.05.011

Index Terms

Box-counting algorithm on GPU and multi-core CPU: an OpenCL cross-platform study
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Graphics processors
  2. Parallel computing methodologies
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language types
        Parallel programming languages

Index terms have been assigned to the content through auto-classification.

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cover image The Journal of Supercomputing

The Journal of Supercomputing Volume 65, Issue 3

Sep 2013

386 pages

ISSN:0920-8542

Issue’s Table of Contents

Copyright © Copyright © 2013 Springer Science+Business Media New York.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2013

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Cited By

Ruiz de Miras J(2020)Fast differential box-counting algorithm on GPUThe Journal of Supercomputing10.1007/s11227-019-03030-176:1(204-225)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1007/s11227-019-03030-1
Duarte-Sanchez JVelasco-Medina JMoreno P(2018)Hardware Accelerator for the Multifractal Analysis of DNA SequencesIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2017.273133915:5(1611-1624)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1109/TCBB.2017.2731339
Jiménez JLópez ACruz JEsteban FNavas JVilloslada PRuiz de Miras J(2014)A Web platform for the interactive visualization and analysis of the 3D fractal dimension of MRI dataJournal of Biomedical Informatics10.1016/j.jbi.2014.05.01151:C(176-190)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.jbi.2014.05.011

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