The Tensor Transpose Compiler (TTC) generates high-performance parallel and vectorized C++ code for multidimensional tensor transpositions.

TTC supports arbitrarily dimensional, out-of-place tensor transpositions of the general form:

where A and B respectively denote the input and output tensor; represents the user-specified transposition, and and being scalars (i.e., setting != 0 enables the user to update the output tensor B).

Please also have a look at TTC-C which provides a C wrapper API for TTC.

Current version: v0.1.1

Attention: Please note that TTC is no longer updated since it has been replaced by an equivalent C++ library: HPTT.

• Generates parallelized and vectorized code
• Support for multiple instruction sets: AVX, AVX2, AVX512, KNC, CUDA
• Support for all datatypes (i.e., single, double, single-complex and double-complex)
• Support for mixed precision (i.e., different data types for A and B)
  • For instance, this feature can be used to generate a mixed-precision BLAS
• Support for multiple leading dimensions in both A and B
  • This enables the user to extract (and transpose) a smaller subtensor out of a larger tensor
• TTC allows the user to guide the code generation process:
  • E.g., specifying the --maxImplementations=N argument will limited the number of generated implementations to N

1. Clone the repository into a desired directory and change to that location:

   git clone https://github.com/HPAC/TTC.git cd TTC

2. Install TTC:

   python setup.py install --user

3. Make sure that you export the TTC_ROOT environment variable (add to your .bashrc):

   export TTC_ROOT=pwd

4. You might have to add the installed location to your PATH environment variable:

   export PATH=$PATH:~/.local/bin

Please run ttc --help to get an overview of TTC's parameters.

Here is one exemplary input to TTC:

ttc --perm=1,0,2 --size=1000,768,16 --dataType=s --alpha=1.0 --beta=1.0 --numThreads=20

You must have a working C compiler. I have tested TTC with:

• Python (tested with v2.7.5 and v2.7.9)
• GCC (>= 4.8)
• Intel's ICC (>= 15.0)

TTC provides a benchmark for tensor transpositions.

python benchmark.py <num_threads>

This will generate the input strings for TTC for each of the test-cases within the benchmark. The benchmark uses a default tensor size of 200 MiB (see _sizeMB variable)

In case you want refer to TTC as part of a research paper, please cite the following article (pdf):

@article{ttc2016a,
   author      = {Paul Springer and Jeff R. Hammond and Paolo Bientinesi},
   title       = {{TTC}: A high-performance Compiler for Tensor Transpositions},
   archivePrefix = "arXiv",
   eprint = {1603.02297},
   primaryClass = "quant-ph",
   journal     = {CoRR},
   year        = {2016},
   issue_date  = {March 2016},
   url         = {http://arxiv.org/abs/1603.02297}
}

V0.1.1:

• Improved performance for streaming stores (only applicable if beta=0)

We are happy for any feedback or feature requests. Please contact springer@aices.rwth-aachen.de.

We also welcome any contributions to the code base.