The Tensor Algebra Compiler (taco) is a C++ library that computes tensor algebra expressions on sparse and dense tensors. It uses novel compiler techniques to get performance competitive with hand-optimized kernels in widely used libraries for both sparse tensor algebra and sparse linear algebra.

You can use taco as a C++ library that lets you load tensors, read tensors from files, and compute tensor expressions. You can also use taco as a code generator that generates C functions that compute tensor expressions.

Learn more about taco at tensor-compiler.org, in the paper The Tensor Algebra Compiler, or in this talk. To learn more about where taco is going in the near-term, see the technical reports on optimization and formats.

You can also subscribe to the taco-announcements email list where we post announcements, RFCs, and notifications of API changes, or the taco-discuss email list for open discussions and questions.

TL;DR build taco using CMake. Run taco-test in the bin directory.

Build taco using CMake 2.8.3 or greater:

cd <taco-directory> mkdir build cd build cmake -DCMAKE_BUILD_TYPE=Release .. make -j8 

To build taco with the Python API (pytaco), add -DPYTHON=ON to the cmake line above. For example:

cmake -DCMAKE_BUILD_TYPE=Release -DPYTHON=ON .. 

You will then need to add the pytaco module to PYTHONPATH:

export PYTHONPATH=<taco-directory>/python_bindings/:$PYTHONPATH 

pytaco requires NumPy and SciPy to be installed.

To build taco with support for parallel execution (using OpenMP), add -DOPENMP=ON to the cmake line above. For example:

cmake -DCMAKE_BUILD_TYPE=Release -DOPENMP=ON .. 

To build taco for NVIDIA CUDA, add -DCUDA=ON to the cmake line above. For example:

cmake -DCMAKE_BUILD_TYPE=Release -DCUDA=ON .. 

Please also make sure that you have CUDA installed properly and that the following environment variables are set correctly:

export PATH=/usr/local/cuda/bin:$PATH export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH export LIBRARY_PATH=/usr/local/cuda/lib64:$LIBRARY_PATH 

If you do not have CUDA installed, you can still use the taco cli to generate CUDA code with the -cuda flag.

To run the C++ test suite:

cd <taco-directory> ./build/bin/taco-test 

To run the Python test suite:

cd <taco-directory> python3 python_bindings/unit_tests.py 

The following sparse tensor-times-vector multiplication example in C++ shows how to use the taco library.

// Create formats Format csr({Dense,Sparse}); Format csf({Sparse,Sparse,Sparse}); Format sv({Sparse}); // Create tensors Tensor<double> A({2,3}, csr); Tensor<double> B({2,3,4}, csf); Tensor<double> c({4}, sv); // Insert data into B and c B.insert({0,0,0}, 1.0); B.insert({1,2,0}, 2.0); B.insert({1,2,1}, 3.0); c.insert({0}, 4.0); c.insert({1}, 5.0); // Pack inserted data as described by the formats B.pack(); c.pack(); // Form a tensor-vector multiplication expression IndexVar i, j, k; A(i,j) = B(i,j,k) * c(k); // Compile the expression A.compile(); // Assemble A's indices and numerically compute the result A.assemble(); A.compute(); std::cout << A << std::endl;

If you just need to compute a single tensor kernel you can use the taco online tool to generate a custom C library. You can also use the taco command-line tool to the same effect:

cd <taco-directory> ./build/bin/taco Usage: taco [options] <index expression> Examples: taco "a(i) = b(i) + c(i)" # Dense vector add taco "a(i) = b(i) + c(i)" -f=b:s -f=c:s -f=a:s # Sparse vector add taco "a(i) = B(i,j) * c(j)" -f=B:ds # SpMV taco "A(i,l) = B(i,j,k) * C(j,l) * D(k,l)" -f=B:sss # MTTKRP Options: ... 

For more information, see our paper on the taco tools taco: A Tool to Generate Tensor Algebra Kernels.