Generate optimized Halide pipelines for Android using ahead-of-time cross-compilation

What you’ll build

In this section, you’ll leverage the host version of Halide to perform AOT compilation of an image processing pipeline via cross-compilation. The resulting pipeline library is specifically tailored to Android devices (targeting, for instance, arm64-v8a ABI), while the compilation itself occurs entirely on the host system. This approach significantly accelerates development by eliminating the need to build Halide or perform JIT compilation on Android devices. It also guarantees that the resulting binaries are optimized for the intended hardware, streamlining the deployment of high-performance image processing applications on mobile platforms.

Learn about ahead-of-time (AOT) and cross-compilation

One of Halide’s standout features is the ability to compile image processing pipelines ahead-of-time (AOT), enabling you to generate optimized binary code on your host machine rather than compiling directly on target devices. This AOT compilation process enables you to create highly efficient libraries that run effectively across diverse hardware without incurring the runtime overhead associated with just-in-time (JIT) compilation.

Halide also supports robust cross-compilation capabilities. Cross-compilation means using the host version of Halide, typically running on a desktop Linux or macOS system—to target different architectures, such as Arm for Android devices. You can optimize Halide pipelines on your host machine, produce libraries specifically optimized for Android, and integrate them seamlessly into Android applications. The generated pipeline code includes essential optimizations and can embed minimal runtime support, further reducing workload on the target device and ensuring responsiveness and efficiency.

Prepare pipeline for Android

The following code demonstrates how to use Halide’s AOT compilation and cross-compilation features to create an optimized image processing pipeline for Android. Run Halide on your host machine (in this example, macOS) to generate a static library containing the pipeline function, which you’ll later invoke from an Android device. Below is a step-by-step explanation of this process.

Create a new file named blur-android.cpp with the following contents:

    

        
        
#include "Halide.h"
#include <iostream>
#include <string>   // for std::string
#include <cstdint>  // for fixed-width integer types (e.g., uint8_t)
using namespace Halide;

int main(int argc, char** argv) {
    if (argc < 2) {
        std::cerr << "Usage: " << argv[0] << " <output_basename> \n";
        return 1;
    }

    std::string output_basename = argv[1];

    // Configure Halide Target for Android
    Halide::Target target;
    target.os = Halide::Target::OS::Android; 
    target.arch = Halide::Target::Arch::ARM;
    target.bits = 64;
    target.set_feature(Target::NoRuntime, false);

    // --- Define the pipeline ---
    // Define variables
    Var x("x"), y("y");

    // Define input parameter
    ImageParam input(UInt(8), 2, "input");

    // Create a clamped function that limits the access to within the image bounds
    Func clamped = Halide::BoundaryConditions::repeat_edge(input);

    // Now use the clamped function in processing
    RDom r(0, 3, 0, 3);
    Func blur("blur");

    // Initialize blur accumulation
    blur(x, y) = cast<uint16_t>(0);
    blur(x, y) += cast<uint16_t>(clamped(x + r.x - 1, y + r.y - 1));

    // Then continue with pipeline
    Func blur_div("blur_div");
    blur_div(x, y) = cast<uint8_t>(blur(x, y) / 9);

    // Thresholding
    Func thresholded("thresholded");
    Expr t = cast<uint8_t>(128);
    thresholded(x, y) = select(blur_div(x, y) > t, cast<uint8_t>(255), cast<uint8_t>(0));

    // Simple scheduling 
    blur_div.compute_root();
    thresholded.compute_root();

    // --- AOT compile to a file ---
    thresholded.compile_to_static_library(
        output_basename,      // base filename
        { input },            // list of inputs
        "blur_threshold",     // name of the generated function
        target
    );

    return 0;
}

    

In the original implementation constants 128, 255, and 0 were implicitly treated as integers. Here, the threshold value (128) and output values (255, 0) are explicitly cast to uint8_t. This approach removes ambiguity and clearly specifies the types used, ensuring compatibility and clarity. Both approaches result in identical functionality, but explicitly casting helps emphasize the type correctness and may avoid subtle issues during cross-compilation or in certain environments. Additionally, explicit uint8_t casts help avoid implicit promotion to 32-bit integers (and the corresponding narrowings back to 8-bit) in the generated code, reducing redundant cast operations and potential vector widen/narrow overhead—especially on Arm/NEON.

The program takes at least one command-line argument, the output base name used to generate the files (for example, “blur_threshold_android”). Here, the target architecture is explicitly set within the code to Android ARM64:

    

        
        
// Configure Halide Target for Android
Halide::Target target;
target.os = Halide::Target::OS::Android; 
target.arch = Halide::Target::Arch::ARM;
target.bits = 64;

// Enable Halide runtime inclusion in the generated library (needed if not linking Halide runtime separately).
target.set_feature(Target::NoRuntime, false);

// Optionally, enable hardware-specific optimizations to improve performance on Arm devices:
// - DotProd: Optimizes matrix multiplication and convolution-like operations on Arm.
// - ARMFp16 (half-precision floating-point operations).

    

Notes:

1. NoRuntime — When set to true, Halide excludes its runtime from the generated code, and you must link the runtime manually during the linking step. When set to false, the Halide runtime is included in the generated library, which simplifies deployment.
2. ARMFp16 — Enables the use of Arm hardware support for half-precision (16-bit) floating-point operations, which improves execution speed when reduced precision is acceptable.
3. Why the runtime choice matters - If your app links several AOT-compiled pipelines, ensure there is exactly one Halide runtime at link time:

• Strategy A (cleanest): build all pipelines with NoRuntime ON and link a single standalone Halide runtime once (matching the union of features you need, for example, Vulkan/OpenCL/Metal or Arm options).
• Strategy B: embed the runtime in exactly one pipeline (leave NoRuntime OFF only there); compile all other pipelines with NoRuntime ON.
• Mixing more than one runtime can cause duplicate symbols and split global state (e.g., error handlers, device interfaces).

The code declares spatial variables (x, y) and an ImageParam named “input” representing the input image data. Boundary clamping (clamp) safely handles edge pixels. A 3×3 blur with a reduction domain (RDom) is then applied. The accumulated sum is divided by 9 (the number of pixels in the neighborhood), producing an average blurred image. Lastly, thresholding is applied, producing a binary output: pixels above a certain brightness threshold (128) become white (255), while others become black (0).

This section intentionally reinforces previous concepts, focusing now primarily on explicitly clarifying integration details, such as type correctness and the handling of runtime features within Halide.

Simple scheduling directives (compute_root) instruct Halide to compute intermediate functions at the pipeline’s root, simplifying debugging and potentially enhancing runtime efficiency.

This strategy can simplify debugging by clearly isolating computational steps and may enhance runtime efficiency by explicitly controlling intermediate storage locations.

By clearly separating algorithm logic from scheduling, you can easily test and compare different scheduling strategies, such as compute_inline, compute_root, compute_at, and more, without modifying your fundamental algorithmic code. This separation significantly accelerates iterative optimization and debugging processes, ultimately yielding better-performing code with minimal overhead.

Halide’s AOT compilation function compile_to_static_library generates a static library (.a) containing the optimized pipeline and a corresponding header file (.h).

    

        
        
thresholded.compile_to_static_library(
    output_basename,      // base filename for output files (e.g., "blur_threshold_android")
    { input },            // list of input parameters to the pipeline
    "blur_threshold",     // the generated function name
    target                // our target configuration for Android
);

    

This will produce:

• A static library (blur_threshold_android.a) containing the compiled pipeline. This static library also includes Halide’s runtime functions tailored specifically for the targeted architecture (arm-64-android). Thus, no separate Halide runtime needs to be provided on the Android device when linking against this library.
• A header file (blur_threshold_android.h) declaring the pipeline function for use in other C++/JNI code.

These generated files are then ready to integrate directly into an Android project via JNI, allowing efficient execution of the optimized pipeline on Android devices. The integration process is covered in the next section.

JNI (Java Native Interface) is a framework that allows Java (or Kotlin) code running in a Java Virtual Machine (JVM), such as on Android, to interact with native applications and libraries written in languages like C or C++. JNI bridges the managed Java/Kotlin environment and the native, platform-specific implementations.

Compile the pipeline

To compile the pipeline-generation program on your host system, use the following commands (replace /path/to/halide with your Halide installation directory):

    

        
        
export DYLD_LIBRARY_PATH=/path/to/halide/lib/libHalide.19.dylib
g++ -std=c++17 blur-android.cpp -o blur-android \
    -I/path/to/halide/include -L/path/to/halide/lib -lHalide \
    $(pkg-config --cflags --libs opencv4) -lpthread -ldl \
    -Wl,-rpath,/path/to/halide/lib

    

Then execute the binary:

    

        
        
./blur_android blur_threshold_android

    

This will produce two files:

• blur_threshold_android.a: The static library containing your Halide pipeline.
• blur_threshold_android.h: The header file needed to invoke the generated pipeline.

You’ll integrate these files into the Android project in the following section.

Summary

You’ve explored Halide’s powerful ahead-of-time (AOT) and cross-compilation capabilities, preparing an optimized image processing pipeline tailored specifically for Android devices. By using the host-based Halide compiler, you generated a static library optimized for 64-bit Arm Android architecture, incorporating safe boundary conditions, neighborhood-based blurring, and thresholding operations. This streamlined process allows seamless integration of highly optimized native code into Android applications, ensuring both development efficiency and runtime performance on mobile platforms.