# mitsuba-renderer/enoki

Enoki: structured vectorization and differentiation on modern processor architectures

Repository: https://github.com/mitsuba-renderer/enoki
Canonical: https://ross.abutalabs.com/products/enoki
Language: C++
License: NOASSERTION
License Family: other
Archived: true
Last push: 2025-02-06T04:46:44+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 5, release rhythm 35, longevity 100
- inputs: {"age_days": 3524, "days_push": 573, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1290, forks 97 (observed 2026-08-28T04:04:15.513299+00:00)

## What it is
Enoki is a C++17 header-only template library for structured vectorization and automatic differentiation of numerical code, compiling the same source to scalar, SIMD (AVX/AVX512/NEON), or CUDA GPU backends. It is now archived and superseded by Dr.Jit.

## Use cases
- vectorize numerical code with SIMD on CPU
- run the same algorithm on GPU via CUDA
- compute gradients with forward or reverse-mode automatic differentiation
- implement differentiable rendering or physics simulations
- write portable high-performance math code with vectors, matrices, and quaternions

## When to choose
- maintaining or extending legacy code already built on Enoki
- you need a header-only C++ SIMD/autodiff library and cannot use Dr.Jit

## When to avoid
- starting a new project - use Dr.Jit instead
- you need actively maintained software or support
- your compiler is older than C++17 requirements

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, compiler, math, gpu-computing
- domain: machine-learning, graphics, simulation, developer-tools
- platform: cpp, windows
- tags: simd, automatic-differentiation, vectorization, header-only, template-library, cuda, scientific-computing, superseded-by-drjit, algorithms, linux, macos, gpu

## Member repositories
- mitsuba-renderer/enoki (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:15.513299+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 2026-08-30T04:55:39.891223+00:00, confidence not recorded.
  - readme: https://github.com/mitsuba-renderer/enoki (fetched 2026-08-28T04:04:15.513299+00:00, sha 0308998ef5e9)
- Data as of 2026-08-30T08:39:29.467469+00:00.