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https://github.com/STEllAR-GROUP/blaze_tensor

3D Tensors for Blaze (https://bitbucket.org/blaze-lib/blaze)
https://github.com/STEllAR-GROUP/blaze_tensor

3d-tensors blaze-library linear-algebra

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3D Tensors for Blaze (https://bitbucket.org/blaze-lib/blaze)

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README 

        



# 3D Tensors for Blaze



This project implements 3D datastructures (tensors) that integrate well with the

[Blaze library](https://bitbucket.org/blaze-lib/blaze/src). 



All the highlights listed for Blaze apply to BlazeTensor as well:



- high performance through the integration of BLAS libraries and manually tuned

  HPC math kernels

- vectorization by SSE, SSE2, SSE3, SSSE3, SSE4, AVX, AVX2, AVX-512, FMA, and SVML

- parallel execution by OpenMP, HPX, C++11 threads and Boost threads

- the intuitive and easy to use API of a domain specific language

- unified arithmetic with dense 3D tensors

- thoroughly tested 3D tensor arithmetic

- completely portable, high quality C++ source code



The implemented facilities are verified using a thorough testing environment.

The [CircleCI](https://circleci.com/gh/STEllAR-GROUP/blaze_tensor) contiguous

integration service tracks the current build status for the master branch:

[![CircleCI](https://circleci.com/gh/STEllAR-GROUP/blaze_tensor.svg?style=svg)](https://circleci.com/gh/STEllAR-GROUP/blaze_tensor).



## What's implemented:



### Datastructures



- `blaze::DynamicArray`: a resizable, row-major ND dense array data structure

  of arbitrary types

- `blaze::CustomArray`: a non-owning ND dense array data structure usable

  to refer to some other ND dense array

- `blaze::DynamicTensor`: a resizable, row-major 3D dense array data structure

  of arbitrary types

- `blaze::CustomTensor`: a non-owning 3D dense array data structure usable

  to refer to some other 3D dense array

- `blaze::StaticTensor`: a statically sized 3D dense array data

  structure of arbitrary types

- `blaze::UniformTensor`: a dynamically sized uniform (all elements have the

  same value) 3D dense array data structure of arbitrary types



### Views



- `blaze::SubTensor<...>`: a view representing a contigous subregion of a dense

  3D array that is statically or dynamically sized

- `blaze::PageSlice<...>`: a view representing a slice of 'thickness' one along

  the row/column plane of a 3D dense array

- `blaze::ColumnSlice<...>`: a view representing a slice of 'thickness' one along

  the page/column plane of a 3D dense array

- `blaze::RowSlice<...>`: a view representing a slice of 'thickness' one along

  the column/page plane of a 3D dense array

- `blaze::DilatedSubvector<...>`: a view representing a sub-vector of any Blaze

  vector-like data structure with an additional 'dilation' (step-size between

  the elements of the underlying vector)

- `blaze::DilatedSubmatrix<...>`: a view representing a sub-matrix of any Blaze

  matrix-like data structure with two additional argumnts: 'rowdilation' and 

  'columndilation' (step-size between the rows and the columns of the underlying

  matrix, respectively).

- `blaze::DilatedSubtensor<...>`: a view representing a sub-tensor of any Blaze

  tensor-like data structure with three additional argumnts: 'pagedilation',  

  'rowdilation' and 'columndilation' (step-size between the pages, rows and the 

  columns of the underlying tensor, respectively).

- `blaze::QuatSlice<...>`: a view representing a slice of 'thickness' one along

  the page/row/column tensor of a 4D dense array

  

### Operations



- All element-wise arithmetic operations that are supported by the Blaze library:

  element-wise addition, subtraction, division, Schur-multiplication, scalar

  multiplication, boolean comparison operations, and many mathematical operations

  like `sqrt`, `cqrt`, `abs`, `sign`, `floor`, etc.

- Reduction operations like `min` and `max`, also unary and binary overloads for

  `blaze::map`.

- Matrix expand operations that produce tensors from expanding (broadcasting)

  matrices in page direction (`blaze::expand()`).

- Matrix and Tensor flattening (`blaze::ravel()`).



We have created a list of things that need to be implemented:

[TODO: Things to implement](https://github.com/STEllAR-GROUP/blaze_tensor/issues/2).

This is a good starting point if you would like to help developing BlazeTensor.



## Building and installing BlazeTensor



In order to use BlazeTensor you will need a proper installation of the

[Blaze library](https://bitbucket.org/blaze-lib/blaze/src). Please see

[here](https://bitbucket.org/blaze-lib/blaze/wiki/Configuration%20and%20Installation)

for instructions on how to install it. Please note that you will need a very

recent version (preferrably top of master) of Blaze. If you want to run the

BlazeTensor tests you will also need the Blaze source directory.



1. Clone this repository

2. Create a build directory

   ```

   $ mkdir build

   $ cd build

   ```

3. Call cmake with the relevant options:

   ```

   $ cmake -DCMAKE_INSTALL_PREFIX=/opt/BlazeTensor -Dblaze_DIR= 

   ```

   here: `` should refer to the directory that contains the file

   `blaze-config.cmake` that was created during installation of Blaze.  

4. Build and install:

   ```

   $ make

   $ make install

   ```

5. If you want to build the tests, additionally specify `-DBLAZETENSOR_WITH_TESTS=ON`

   and `-Dblazetest_DIR=` 

   on the `cmake` command line. Run the tests with `make tests`.

   

BlazeTensor is a header only C++ library. Projects depending on it should make

sure the headers are being found by the compiler. If your depending project uses

`cmake`, just add `find_package(BlazeTensor)` to your scripts and refer to the

target named `BlazeTensor::BlazeTensor`.



## Communication

A channel on the freenode IRC network is used for discussions on

BlazeTensor: #ste||ar on freenode (via SSL). Feel free to use the

[Github issue tracker](https://github.com/STEllAR-GROUP/blaze_tensor/issues) for

questions, bug reports, and feature requests. 



## License

BlazeTensor is released under the terms of the

[New (Revised) BSD license](https://github.com/STEllAR-GROUP/blaze_tensor/blob/master/LICENSE).



## Acknowledgements



We would like to acknowledge the NSF, DoD, and the Center for Computation

and Technology (CCT) at Louisiana State University (LSU).



BlazeTensor is currently funded by:



* The National Science Foundation through awards 1737785 (Phylanx).



  Any opinions, findings, and conclusions or recommendations expressed in this

  material are those of the author(s) and do not necessarily reflect the views

  of the National Science Foundation.

  

* The Defense Technical Information Center (DTIC) under contract FA8075-14-D-0002/0007



  Neither the United States Government nor any agency thereof, nor any of their 

  employees makes any warranty, express or implied, or assumes any legal liability 

  or responsibility for the accuracy, completeness, or usefulness of any information, 

  apparatus, product, or process disclosed, or represents that its use would not 

  infringe privately owned rights.