Halide: A novel programming paradigm for advanced image processing

Introduction

Performance matters. Whether you are a software engineer working on real-time image processing applications, or a researcher or academic exploring efficient algorithm implementations. Developers traditionally relied on custom libraries with optimized operations and used general-purpose languages like C++, both of which required navigating complex optimization challenges.

Today, there is another option: Halide. It is a powerful, open-source image processing language designed to streamline performance without sacrificing readability.

What makes Halide different?

Halide stands out because it separates traditional image processing into two core concepts:

• Algorithm: what you want to do.
• Schedule: how to optimize and execute it.

This separation allows you to clearly define image processing logic separately from hardware-specific optimization strategies. Unlike traditional programming methods, changes to how computations are executed do not require rewriting the algorithm itself.

Key advantages of Halide

Halide provides high performance by enabling optimizations through defining the schedules for an algorithm. Advanced loop transformations, vectorization, and parallel execution are just a function call away. With a few simple scheduling directives you can match the speed of meticulously hand-optimized C++ code.

Halide’s modular structure enables you to easily experiment with different performance strategies. You can optimize your algorithms differently for different hardware targets. Halide supports several architectures, such as x86, Arm, WebAssembly, and operating systems, such as Windows, Linux, or Android. You can also use it in heterogeneous computing with targets such as GPU and DSP. The separation of schedule and algorithm in Halide significantly simplifies cross-platform performance tuning.

With Halide, your image processing algorithm remains clean and readable, closely resembling the mathematical operations you are performing. The resulting code is easy to maintain, understand, and debug. This can reduce overall development time.

Consider a simple blur operation from Halide’s own introduction (https://halide-lang.org):

Func blur(Func input) {
    Var x, y, xi, yi;
    Func blur_x, blur_y;

    blur_x(x, y) = (input(x - 1, y) + input(x, y) + input(x + 1, y)) / 3;
    blur_y(x, y) = (blur_x(x, y - 1) + blur_x(x, y) + blur_x(x, y + 1)) / 3;
    blur_y.tile(x, y, xi, yi, 256, 32).vectorize(xi, 8).parallel(y);
    blur_x.compute_at(blur_y, x).vectorize(x, 8);

   return blur_y;
}

The algorithm defines the blur operation in two stages. The schedule handles optimization like tiling, parallelization, and vectorization. Changes in the schedule do not need any modifications to the algorithm. No matter how you schedule this simple pipeline, the output will always be the same.

Writing image processing algorithms directly in C++ or with the use of custom libraries requires significant expertise, time, and maintenance. Halide offers important advantages in these cases. Halide performs complex optimizations and delivers superior performance with minimal effort. Writing and maintaining separate implementations for multiple hardware platforms is cumbersome. Halide enables a single algorithm and different schedules to efficiently target diverse hardware and simplify multi-platform support.

Writing your own schedule from scratch can seem daunting if you are a beginner, but Halide solves that issue as well. The language includes Autoschedulers. These are loadable plugins, which aim to create an optimal schedule for your pipeline. The best part is they are available as an includable header file. You can further modify the schedule if you want to.

Getting started

Getting started with Halide is straightforward:

• Since Halide is implemented through the C++ API, including Halide in existing C++ projects is as simple as including a new header file and linking against the library during build.
• You can get started with the hands-on Halide Learning Path (see link below): Set up your system, create a basic real-time image processing pipeline using Halide. Then optimize it and integrate the Halide pipelines into an Android application.
• For reference, there are a series of Halide tutorials: https://halide-lang.org/tutorials.
• If you already have a pipeline that you want to rewrite using Halide, try out Autoschedulers to get a working and performant example quickly.

Halide is more than just another programming tool. It is a paradigm shift in image processing. Whether you are developing computer vision applications, real-time image processing, or computational photography, Halide can simplify the development process while delivering high performance. Give Halide a try and experience the advantages of this new approach to image processing.

Build high-performance image processing with Halide on Android