NOTE: this is the development repository for the streaming version of
SPLATT. Run splatt cpd --help
for streaming instructions. In short, you can
add the --stream=<MODE> --reg=frob,1e-2,<MODE>
flag to stream one tensor mode
with regularization (strongly recommended). This is a prototype implementation
of the streaming algorithm, and thus the tensor is still read from disk in its
entirety and then factored in a streaming manner.
The streaming algorithm implemented in this library was published in SDM'18: "Streaming Tensor Factorization for Infinite Data Sources". If this contributes to your research, please cite with the following BibTeX entry:
@inproceedings{smith2018streaming,
title={Streaming Tensor Factorization for Infinite Data Sources},
author={Smith, Shaden and Huang, Kejun and Sidiropoulos, Nicholas D. and Karypis, George},
booktitle={Proceedings of the 2018 SIAM International Conference on Data Mining},
year={2018},
organization={SIAM}
}
SPLATT is a library and C API for sparse tensor factorization. SPLATT supports shared-memory parallelism with OpenMP and distributed-memory parallelism with MPI.
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
SPLATT requires CMake and working BLAS/LAPACK libraries to run. In short,
$ ./configure && make
will build the SPLATT library and its executable. SPLATT also provides options to automatically download the CMake and BLAS/LAPACK dependencies on Linux/OSX systems:
$ ./configure --download-cmake --download-blas-lapack
You can also run
$ ./configure --help
to see additional build options. After compilation, the executable will be
found in build/<arch>/bin/
. 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.
$ 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.
$ 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
.
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:
$ 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:
$ 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 libsplatt.so
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 filesplatt_csf_convert
converts a tensor from coordinate format to CSF
splatt_cpd
computes the CPD and returns a Kruskal tensorsplatt_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 tensorssplatt_free_opts
deallocates a SPLATT options arraysplatt_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);
splatt_free_kruskal(&factored);
splatt_free_opts(cpd_opts);
Please see splatt.h
for further documentation of SPLATT structures and call
signatures.
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:
-
Ensure that your OpenMP library is loaded first when starting Matlab. The most common OpenMP library is
libgomp.so.1
:$ LD_PRELOAD=libgomp.so.1 matlab
-
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 matriceslambda
the factor column norms absorbed into a vectorfit
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.
SPLATT is released under the MIT License. Please see the 'LICENSE' file for details.