AI Potluck
Infrastructure / Compilers & Model Optimization

TACO

TACO (MIT CSAIL / Adobe)

TACO (the Tensor Algebra Compiler) is a C++ library and code generator, developed at MIT CSAIL and Adobe, that compiles sparse and dense tensor algebra expressions into native code competitive with hand-optimized kernels, using novel sparse iteration-space compiler techniques.

Verified 2026-09-02 via the GitHub API and the LICENSE body.

Openness

5 high confidence
5.0
license
MIT(OSI)
source
public
core-gated
ungated

MIT license body confirmed. The repository is public and unarchived and builds the whole product, and the README describes no paid tier, enterprise edition or license-gated build beside it, so source is public and the core ungated.

Adoption

2 low confidence
2.0

1,367 GitHub stars, which lands in the 1K-10K stars band of the stars scale, level 2. The `taco` package on PyPI is an unrelated project by another author, and TACO itself ships only as a source build, so no download count exists and the stars scale applies.

Capability

4 medium confidence
4.0

One band below the apache-tvm anchor: a full compilation toolchain - front end through code generation - narrowed to one class of programs, sparse and dense tensor algebra, rather than apache-tvm's retargetable multi-framework, multi-vendor scope.

  • https://github.com/tensor-compiler/taco/blob/master/README.md recorded 2026-09-02

    README describes taco as a C++ library that computes sparse and dense tensor algebra expressions with performance competitive with hand-optimized kernels, usable as a library or as a C code generator, with no paid tier, enterprise edition or license-gated build beside the published source.

Verified 2026-09-02