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Thesis

Accelerating sequential machine vision algorithms
using commodity parallel hardware


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Keywords: Computer Vision, Image processing, Parallel programming, Multi-core CPU, GPU, C++, OpenMP, OpenCL.

Many other related research projects have considered using one domain specific algorithm to compare the best sequential implementation with the best parallel implementation on a specific hardware platform. This work was distinctive because it investigated how to speed up a whole library by parallelizing the algorithms in an economical way and execute them on multiple platforms.

This work has:
  • Examined, compared and evaluated 22 programming languages and environments for parallel computing on multi-core CPUs and GPUs.
  • Chosen to use OpenMP as the standard for multi-core CPU programming and OpenCL for GPU programming.
  • Re-implemented a number of standard and well-known algorithms in Computer Vision using both standards.
  • Tested the performance of the implemented parallel algorithms and compared the performance to the sequential implementations of the software package VisionLab.
  • Evaluated the test results with a view to assessing:
    • Appropriateness of multi-core CPU and GPU architectures in Computer Vision.
    • Benefits and costs of parallel approaches to implementation of Computer Vision algorithms.

Using OpenMP it was demonstrated that many algorithms of a library could be parallelized in an economical way and that adequate speedups were achieved on two multi-core CPU platforms. With a considerable amount of extra effort, OpenCL was used to achieve much higher speedups for specific algorithms on dedicated GPUs.

At the end of the project, the choice of standards was re-evaluated including newly emerged ones. Recommendations are given for using standards in the future, and for future research and development.

Algorithmic improvements are suggested for Convolution and Connect Component Labelling.

This work resulted directly in innovations in the product VisionLab:
  • 170 operators were parallelized using OpenMP and Automatic Operator Parallelization was implemented. Users of VisionLab can now benefit from parallelization without having to rewrite their scripts, C++ or C# code.
  • OpenCL toolbox was added to the development environment. Users of VisionLab can now comfortably write OpenCL host-side code using the script language and edit their kernels. The OpenCL host interface was implemented and tested for NVIDIA, AMD and Intel OpenCL platforms.

Course material about acceleration vision algorithms using OpenMP and OpenCL.

Your feedback and/or questions are welcome at
jaap@vdlmv.nl or j.van.de.loosdrecht@nhl.nl