CUDA-accelerated Boolean Matrix Factorization
Boolean Matrix Factorization (BMF) is a commonly used technique in the field of unsupervised data analytics. The goal is to decompose a ground truth matrix C of shape m × n into a product of two matrices A and B being either an exact or approximate rank k factorization of C.
cuBool is based on alternately adjusting rows and columns of A and B using thousands of lightweight CUDA threads. The massively parallel manipulation of entries enables full usage of all available cores on modern CUDA-enabled GPUs. Additionally, modelling up to 32 consecutive entries of the Boolean matrices A, B and C as 32-bit integer results in fewer data accesses and faster computation of inner products. This bit-parallel approach allows for a significant decrease of memory requirements in contrast to gradient-based continuous updates of entries on dense repre- sentations.
cuBool is a further development of https://github.com/alamoth/CuBin.
cuBool was tested with CUDA 9.2 and g++-7. Before using make, be sure to adjust the NVCC -arch flag in the Makefile to your GPU achitecture.
To compile GPU version:
make cuBool
To compile CPU version (can be compiled without CUDA):
make cuBool_cpu
Call ./cuBool or ./cuBool_cpu without parameters to see all parameter options.
Required structure of dataset file:
- First line defines matrix:
<rows> <column> <number of nonzeros> - Every other line defines coordinates of a single entry:
<row> <column>