TACO is a library for performing sparse and dense linear algebra and tensor algebra computations. The computations can range from relatively simple ones like sparse matrix-vector multiplication to more complex ones like matricized tensor times Khatri-Rao product. All these computations can be performed on any mix of dense and sparse tensors. Under the hood, TACO automatically generates efficient code to perform these computations.

The sidebar to the left links to documentation for the TACO C++ and Python APIs as well as some examples demonstrating how TACO can be used in real-world applications.

System Requirements

  • A C compiler that supports C99, such as GCC or Clang
    • Support for OpenMP is also required if parallel execution is desired
  • Python 3 with NumPy and SciPy (for the Python API)

Getting Help

Questions and bug reports can be submitted here.