# halide/Halide

a language for fast, portable data-parallel computation

Repository: https://github.com/halide/Halide
Canonical: https://ross.abutalabs.com/products/halide
Homepage: https://halide-lang.org
Language: C++
License: NOASSERTION
License Family: other
Topics: halide, hexagon, compiler, dsl, gpu, image-processing, performance
Last push: 2026-08-26T22:01:50+00:00

## Health v2 (maintenance only)
Score: 70/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 16, longevity 100
- inputs: {"age_days": 5146, "days_push": 7, "days_rel": 351, "gap_med": 273, "n_releases_24m": 2}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 6590, forks 1105 (observed 2026-08-28T04:09:45.529664+00:00)

## What it is
Halide is an embedded DSL (in C++ and Python) for writing high-performance, data-parallel image and array processing pipelines. It separates the algorithm from its schedule, compiling pipelines ahead-of-time or via JIT to CPUs (x86, ARM, Hexagon, RISC-V, WebAssembly) and GPUs (CUDA, OpenCL, Metal, DirectX 12, Vulkan).

## Use cases
- write fast image processing pipelines
- optimize code for cpu and gpu without rewriting
- schedule separable image filters like blurs
- compile image processing for mobile dsp (hexagon)
- accelerate tensor and array computations
- cross-compile performance kernels for multiple platforms
- auto-schedule image processing code

## When to choose
- you need high-performance image or array processing across cpu and gpu targets
- you want to tune performance (tiling, vectorization, parallelism) separately from algorithm code
- you target heterogeneous hardware including DSPs and mobile GPUs
- you want JIT or ahead-of-time compilation of processing pipelines

## When to avoid
- you need a general-purpose programming language for arbitrary applications
- your workload is scalar control-flow-heavy rather than dense array computation
- you want a simple drop-in library without learning scheduling concepts
- you need small, dependency-free image utilities

## Facets
- artifact type: library
- maturity: stable
- function: compiler, image-processing, gpu-computing, programming-language, math
- domain: image-processing, compilers, performance, computer-vision, gpu-computing, programming-languages
- platform: cpp, python, windows, wasm, cross-platform
- tags: dsl, data-parallel-computation, scheduling, aot-compilation, jit-compilation, auto-scheduler, tensor-processing, embedded-language, linux, macos, android, ios, gpu

## Member repositories
- halide/Halide (main) score 70

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:45.529664+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:43:34.073130+00:00, confidence not recorded.
  - readme: https://github.com/halide/Halide (fetched 2026-08-28T04:09:45.529664+00:00, sha 4bbdcbf27dea)
  - homepage: https://halide-lang.org (fetched 2026-08-29T08:39:55.147993+00:00, sha 08ff8e3b75e4)
  - site_page: https://halide-lang.org/docs/tutorial/index.html (fetched 2026-08-29T08:39:55.151040+00:00, sha 34d7af4b51f5)
  - site_page: https://halide-lang.org/docs (fetched 2026-08-29T08:39:55.152996+00:00, sha f0d28152844e)
- Data as of 2026-08-30T08:39:29.467469+00:00.