Install halide with Homebrew, apt, Nix, pacman

Project links

• Homepage
• Repository
• Upstream docs
• Package manager page

Commands and aliases

• adams2019_retrain_cost_model
• adams2019_weightsdir_to_weightsfile
• anderson2021_retrain_cost_model
• anderson2021_weightsdir_to_weightsfile
• featurization_to_sample
• gengen
• get_host_target

Project history

MIT News introduced Halide in 2012 as a programming language for image-processing software, reporting that MIT researchers used it to express common image-processing algorithms with shorter code and significant speedups. The Halide site presents the same core idea in tool form: write a pipeline in C++ using Halide's API, then JIT-compile it or compile it ahead of time.

The research lineage is central to the project. The Halide site lists publications on decoupling algorithms from schedules, the 2013 PLDI language and compiler paper, automatic scheduling work from 2016, differentiable programming work from 2018, and machine-learning-based autoscheduling in 2019. Those papers explain why Halide is not just a library of filters but a compiler approach to high-performance pipelines.

The release history shows the compiler growing from research artifact into a packaged toolchain. A 2013 GitHub release provided precompiled Halide builds. Later releases added WebAssembly, Direct3D, automatic differentiation, PyTorch custom-op generation, multiple autoschedulers as plugins, C++17 requirements, Vulkan and WebGPU work, RISC-V vector support, Python packaging through PyPI, and alignment of major version numbers with bundled LLVM versions.

Adoption history

Halide's adoption spans research, imaging applications, compiler experimentation, and performance-sensitive production code. The official site points users to tutorials, example apps, tests, GitHub Discussions, CppCon material, CVPR course notes, and API docs, which reflects a community built around learning the scheduling model as much as installing a binary.

For package managers, Halide became more approachable as upstream added binary tarballs, Homebrew installation instructions, vcpkg support, and PyPI wheels for C++ and Python use. Those distribution paths matter because building Halide from source requires a compatible LLVM toolchain and substantial compiler infrastructure.

How it is used

Users define Halide Func, Var, and schedule objects in host code, then compile pipelines for CPU, GPU, mobile, and other targets. The official README describes supported CPU architectures, operating systems, GPU compute APIs, C++17 requirements, Python bindings, tutorials, apps, and build instructions.

Installed package binaries are often used by developers who want headers, libraries, tools such as generators, and CMake integration without managing a custom LLVM build. The Homebrew formula is especially useful on macOS where LLVM discovery and CMake presets can otherwise dominate the first build.

Why package nerds care

Halide is significant because it turns a compiler research system into a package-manager-installable dependency. The formula is not just a CLI; it packages a DSL runtime, compiler libraries, generators, examples, and an LLVM-facing build surface.

It also shows why some packages are hard to reduce to a single executable. Halide's value is in the development workflow: stable headers, linkable libraries, target backends, Python wheels, CMake discovery, and compatibility with the LLVM versions upstream supports.

Timeline

• 2012: MIT News described Halide as a language for easier and faster image-processing software.
• 2013: PLDI paper described Halide as a language and compiler for optimizing parallelism, locality, and recomputation.
• 2013: GitHub release provided precompiled Halide builds.
• 2016: Automatic scheduling research expanded the Halide scheduling workflow.
• 2018: Halide release added features such as PyTorch custom-op generation, automatic differentiation, and WebAssembly support.
• 2019: SIGGRAPH autoscheduler work applied tree search and random programs to Halide optimization.
• 2021: Halide 13 required C++17 and documented Homebrew and vcpkg package paths.
• 2023: Halide 15 added PyPI-distributed Python builds and RISC-V vector support.
• 2023: Halide 16 added Vulkan support, experimental WebGPU support, and a GPU autoscheduler.
• 2025: Halide 21 aligned the major version with LLVM and continued Python-binding improvements.

Related projects

• LLVM: compiler infrastructure used by Halide for code generation.
• CMake and vcpkg: build and packaging paths documented by Halide.
• PyPI halide package: binary distribution route for C++ and Python use.
• PyTorch: target ecosystem referenced by Halide release notes for custom operators.
• CUDA, OpenCL, Metal, DirectX, Vulkan, WebAssembly, and Hexagon: target backends or platforms represented in Halide documentation and releases.

Sources

• https://dl.acm.org/doi/10.1145/2499370.2462176
• https://dl.acm.org/doi/10.1145/3150211
• https://github.com/halide/Halide
• https://github.com/halide/Halide/releases
• https://halide-lang.org/
• https://halide-lang.org/docs
• https://halide-lang.org/papers/halide_autoscheduler_2019.pdf
• https://news.mit.edu/2012/better-programming-language-for-image-processing-0802
• https://raw.githubusercontent.com/halide/Halide/main/README.md