Efficient Image Processing with Halide

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Halide is a programming language for image processing that aims to provide high-performance code across platforms. It separates the algorithm definition from the schedule to allow for architecture-specific optimization. Evaluation shows Halide outperforms other tools like ImageMagick and MATLAB for RGB to grayscale and Gaussian blur operations on test images, with execution times within 1-10% of the other fastest tool, OpenCV 2. Halide requires more learning than other tools but allows for easier optimization through its separation of algorithm and schedule.

Technology Art & Photos

Efficient Image
Processing with
Halide
Presented by Adrián Palacios

Introduction
• Image processing is a topic
where optimization matters.
• But optimization for multiple
platforms is hard and
expensive.
• We want tools for obtaining
high-performance code
regardless of the platform.
• Halide is a tool that aims to
solve this problem.

Data-parallel and IP languages
• Data-parallel languages:
• CUDA and OpenCL propose a SIMD
programming model for multi-core CPUs
and GPUs.
• Implementations can be very efficient at
the cost of losing portability.
• IP languages:
• MATLAB and other suites release kernel
languages.
• But individual kernels are not enough.

The Halide language
• Halide is a functional programming language (“à
la Haskell”) for IP.
• It makes a distinction between the algorithm and
the schedule:
• The algorithm is what should be done.
• The schedule is how it should be done.
• Optimization is achieved by:
• Using LLVM for generating simple code.
• Using architecture-specific compilers for
generating vectorized and parallel code.

Evaluation of Halide
• Halide’s execution time is measured against:
• ImageMagick.
• MATLAB.
• Mathematica.
• OpenCV 2.
• Two test images:
• A normal sized image (512x512).
• A big sized image (6400x4800).
• For two methods:
• RGB to grayscale.
• Gaussian blur.

RGB to grayscale results
Normal sized image Time (ms) Time / Halide Time (%)
Halide 8.486 1.000
ImageMagick 64.000 7.542
MATLAB 10.359 1.221
Mathematica 13.000 1.532
OpenCV 2 0.577 0.067
Big sized image Time (ms) Time / Halide Time (%)
Halide 188.829 1.000
ImageMagick 1748 9.257
MATLAB 192.501 1.019
Mathematica 1586 8.399
OpenCV 2 76.626 0.405

Gaussian blur results
Normal sized image Time (ms) Time / Halide Time (%)
Halide 2.674 1.000
ImageMagick 304.000 113.687
MATLAB 2.834 1.059
Mathematica 117.003 43.755
OpenCV 2 1.076 0.402
Big sized image Time (ms) Time / Halide Time (%)
Halide 219.274 1.000
ImageMagick 12265 55.935
MATLAB 277.388 1.265
Mathematica 199203 908.466
OpenCV 2 191.875 0.875

Conclusions
• Halide beats each other tool
(except OpenCV 2).
• There’s a lot of room for
improvement.
• Programming with Halide is
hard-to-learn, easy-to-
master.

Questions?
• Halide’s repository at Github:
• https://github.com/halide/Halide

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Efficient Image Processing with Halide

1. Efficient Image Processing with Halide Presented by Adrián Palacios

2. Introduction • Image processing is a topic where optimization matters. • But optimization for multiple platforms is hard and expensive. • We want tools for obtaining high-performance code regardless of the platform. • Halide is a tool that aims to solve this problem.

3. Data-parallel and IP languages • Data-parallel languages: • CUDA and OpenCL propose a SIMD programming model for multi-core CPUs and GPUs. • Implementations can be very efficient at the cost of losing portability. • IP languages: • MATLAB and other suites release kernel languages. • But individual kernels are not enough.

4. Concretely, the problem is…

5. And the solution is…

6. The Halide language • Halide is a functional programming language (“à la Haskell”) for IP. • It makes a distinction between the algorithm and the schedule: • The algorithm is what should be done. • The schedule is how it should be done. • Optimization is achieved by: • Using LLVM for generating simple code. • Using architecture-specific compilers for generating vectorized and parallel code.

7. Evaluation of Halide • Halide’s execution time is measured against: • ImageMagick. • MATLAB. • Mathematica. • OpenCV 2. • Two test images: • A normal sized image (512x512). • A big sized image (6400x4800). • For two methods: • RGB to grayscale. • Gaussian blur.

8. RGB to grayscale

9. RGB to grayscale results Normal sized image Time (ms) Time / Halide Time (%) Halide 8.486 1.000 ImageMagick 64.000 7.542 MATLAB 10.359 1.221 Mathematica 13.000 1.532 OpenCV 2 0.577 0.067 Big sized image Time (ms) Time / Halide Time (%) Halide 188.829 1.000 ImageMagick 1748 9.257 MATLAB 192.501 1.019 Mathematica 1586 8.399 OpenCV 2 76.626 0.405

10. Gaussian blur

11. Gaussian blur results Normal sized image Time (ms) Time / Halide Time (%) Halide 2.674 1.000 ImageMagick 304.000 113.687 MATLAB 2.834 1.059 Mathematica 117.003 43.755 OpenCV 2 1.076 0.402 Big sized image Time (ms) Time / Halide Time (%) Halide 219.274 1.000 ImageMagick 12265 55.935 MATLAB 277.388 1.265 Mathematica 199203 908.466 OpenCV 2 191.875 0.875

12. Conclusions • Halide beats each other tool (except OpenCV 2). • There’s a lot of room for improvement. • Programming with Halide is hard-to-learn, easy-to- master.

13. Questions? • Halide’s repository at Github: • https://github.com/halide/Halide

Efficient Image Processing with Halide

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