A CUDA implementation of non-negative matrix factorization for GPUs.
A description of the implementation is available in [1] below. If you use this in your work, please cite [1].
[1] E. Battenberg and D. Wessel, “Accelerating non-negative matrix factorization for audio source separation on multi-core and many-core architectures,” in International Society for Music Information Retrieval Conference (ISMIR 2009), 2009.
Bibtex entry: ''' @inproceedings{battenberg2009accelerating, title={Accelerating nonnegative matrix factorization for audio source separation on multi-core and many-core architectures}, author={Battenberg, E. and Wessel, D.}, booktitle={10th International Society for Music Information Retrieval Conference (ISMIR 2009)}, year={2009} } '''
#Implementation Details:
Iterative NMF on Cuda: X = W*H
multiplicative updates
divergence cost function
nmf.cu contains an example usage of the update_div function.
The function read_matrix reads binary floats in from files and stores them in a matrix struct
- values need to be stored in column-major order, and the first two values in the file are integer dimensions of the matrix
- an example of how to create the binary files (from e.g. Matlab data) is contained in the matlab script matrix_export.m
- data can also just be stored in a column-major float array using the matrix struct and proper assignment of the dim values
The function update_div is where the work is done.
void update_div(
matrix W, matrix H,
matrix X,
float CONVERGE_THRESH,
int max_iter,
double t[10],
int verbose);- W, H are initial values for the factor matrices
- X is the target matrix to be decomposed
- CONVERGE_THRESH is the convergence threshold (expressed as a ratio of cost function change to cost function value)
- max_iter is the maximum number of iterations
- double t[10] is a pointer to a double array of at least size 10 that will contain individual timing results for different computational pieces. (set this to NULL for normal use)
- verbose set to 1 if you want more text output, 0 otherwise