The Surprisingly ParalleL spArse Tensor Toolkit.
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The Surprisingly ParalleL spArse Tensor Toolkit

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SPLATT is a library and C API for sparse tensor factorization. SPLATT supports shared-memory parallelism with OpenMP and distributed-memory parallelism with MPI.

Tensor Format

SPLATT expects tensors to be stored in 0- or 1-indexed coordinate format with nonzeros separated by newlines. Each line of of the file has the coordinates of the nonzero followed by the value, all separated by spaces. The following is an example 2x2x3 tensor with 5 nonzeros:

# This is a comment
1 1 2 1.5
1 2 2 2.5
2 1 1 3.7
1 2 3 0.5
2 1 2 4.1

Building & Installing

SPLATT requires CMake and working BLAS/LAPACK libraries to run. In short,

$ ./configure && make

will build the SPLATT library and its executable. The executable will be found in build/<arch>/bin/. You can also run

$ ./configure --help

to see additional build options. To install,

$ make install

will suffice. The installation prefix can be chosen by adding a '--prefix=DIR' flag to configure.


After building, an executable will found in the build/ directory (or the installation prefix if SPLATT was installed). SPLATT builds a single executable which features a number of sub-commands:

  • cpd
  • check
  • convert
  • reorder
  • stats

All SPLATT commands are executed in the form

$ splatt CMD [OPTIONS]

You can execute

$ splatt CMD --help

for usage information of each command.

Example 1

$ splatt check mytensor.tns  --fix=fixed.tns

This runs splatt-check on 'mytensor.tns' and writes the fixed tensor to 'fixed.tns'. The splatt-check routine finds empty slices and duplicate nonzero entries. Empty slices are indices in any mode which do not have any nonzero entries associated with them. Some SPLATT routines (including CPD) expect there to be no empty slices, so running splatt-check on a new tensor is recommended.

Example 2

$ splatt cpd mytensor.tns -r 25 -t 4

This runs splatt-cpd on 'mytensor.tns' and finds a rank-25 CPD of the tensor. Adding '-t 4' instructs SPLATT to use four OpenMP threads during the computation. SPLATT will use all available CPU cores by default. The matrix factors are written to modeN.mat and lambda, the vector for scaling, is written to lambda.mat.

Distributed-Memory Computation

SPLATT can optionally be built with support for distributed-memory systems via MPI. To add MPI support, simply add "--with-mpi" to the configuration step:

$ ./configure --with-mpi && make

After building with MPI, splatt-cpd can be used as before. Careful consideration should be given to the mapping of MPI ranks, because each SPLATT process will by default use all available CPU cores ($OMP_MAX_THREADS). We recommend mapping one rank per CPU socket. The necessary parameters to mpirun vary based on the MPI implementation. For example, OpenMPI supports:

Example 3

$ mpirun --map-by ppr:1:socket -np 16 splatt cpd mytensor.tns -r 25 -t 8

This would fully utilize 16 sockets, each with 8 cores to compute a rank-25 CPD of mytensor.tns. To alternatively use one MPI rank per core:

Example 4

$ mpirun -np 128 splatt cpd mytensor.tns -r 25 -t 1

This would use 128 processes, with each using only one OpenMP thread.


SPLATT provides a C API which is callable from C and C++. Installation not only installs the SPLATT executable, but also the shared library and the header splatt.h. To use the C API, include splatt.h and link against the SPLATT library.


Unless otherwise noted, SPLATT expects tensors to be stored in the compressed sparse fiber (CSF) format. SPLATT provides two functions for forming a tensor in CSF:

  • splatt_csf_load reads a tensor from a file
  • splatt_csf_convert converts a tensor from coordinate format to CSF


  • splatt_cpd computes the CPD and returns a Kruskal tensor
  • splatt_default_opts allocates and returns an options array with defaults


All memory allocated by the SPLATT API should be freed by these functions:

  • splatt_free_csf deallocates a list of CSF tensors
  • splatt_free_opts deallocates a SPLATT options array
  • splatt_free_kruskal deallocates a Kruskal tensor


The following is an example usage of the SPLATT API:

#include <splatt.h>

/* allocate default options */
double * cpd_opts = splatt_default_opts();

/* load the tensor from a file */
int ret;
splatt_idx_t nmodes;
splatt_csf_t * tt;
ret = splatt_csf_load("mytensor.tns", &nmodes, &tt, cpd_opts);

/* do the factorization! */
splatt_kruskal_t factored;
ret = splatt_cpd_als(tt, 10, cpd_opts, &factored);

/* do some processing */
for(splatt_idx_t m = 0; m < nmodes; ++m) {
  /* access factored.lambda and factored.factors[m] */

/* cleanup */
splatt_free_csf(tt, cpd_opts);

Please see splatt.h for further documentation of SPLATT structures and call signatures.

Octave/Matlab API

SPLATT also provides an API callable from Octave and Matlab that wraps the C API. To compile the interface just enter the matlab/ directory from either Octave or Matlab and call make.

>> cd matlab
>> make

NOTE: Matlab uses a version of LAPACK/BLAS with 64-bit integers. Most LAPACK/BLAS libraries use 32-bit integers, and so SPLATT by default provides 32-bit integers. You should either instruct Matlab to link against a matching library, or configure SPLATT to also use 64-bit integers during configuration:

$ ./configure --blas-int=64

Note that this may break usability of the SPLATT executable or API.

Some Matlab versions have issues with linking to applications which use OpenMP (e.g., SPLATT) due to a limited amount of thread-local storage. This is a system limitation, not necessarily a software limitation. When calling SPLATT from Matlab, you may receive an error message:

dlopen: cannot load any more object with static TLS

Two workarounds for this issue are:

  1. Ensure that your OpenMP library is loaded first when starting Matlab. The most common OpenMP library is

     $ matlab 
  2. Disable OpenMP (at the cost of losing multi-threaded execution):

     $ ./configure --no-openmp

After compilation, the MEX files will be found in the current directory. You can now call those functions directly:

>> KT = splatt_cpd('mytensor.tns', 25);

splatt_cpd returns a structure with three fields:

  • U a cell array of the factor matrices
  • lambda the factor column norms absorbed into a vector
  • fit quality of the CPD defined by: 1 - (norm(residual) / norm(X))

SPLATT also supports explicitly storing tensors in CSF form to avoid IO times during successive factorizations,

>> X = splatt_load('mytensor.tns');
>> K25 = splatt_cpd(X, 25);
>> K50 = splatt_cpd(X, 50);

SPLATT accepts non-default parameters via structures:

>> opts = struct('its', 100, 'tol', 1e-8);
>> XT = splatt_cpd(X, 25, opts);

Finally, there are several SPLATT routines exposed for developing other tensor operations. SPLATT provides:

  • splatt_mttkrp
  • splatt_norm
  • splatt_dim
  • splatt_innerprod

Please see help <cmd> from an Octave/Matlab terminal or read .m in the matlab/ directory for more usage information.


If SPLATT has contributed to your research, please consider citing us:

    author = {Smith, Shaden and Karypis, George},
    title = {{SPLATT: The Surprisingly ParalleL spArse Tensor Toolkit}},
    howpublished = {\url{}},
    version = {1.1.1},
    year = {2016},


SPLATT is released under the MIT License. Please see the 'LICENSE' file for details.