Non negative least squares on GPU/multicore architectures

September 4th, 2011


We parallelize a version of the active-set iterative algorithm derived from the original works of Lawson and Hanson (1974) on multi-core architectures. This algorithm requires the solution of an unconstrained least squares problem in every step of the iteration for a matrix composed of the passive columns of the original system matrix. To achieve improved performance, we use parallelizable procedures to efficiently update and {\em downdate} the QR factorization of the matrix at each iteration, to account for inserted and removed columns. We use a reordering strategy of the columns in the decomposition to reduce computation and memory access costs. We consider graphics processing units (GPUs) as a new mode for efficient parallel computations and compare our implementations to that of multi-core CPUs. Both synthetic and non-synthetic data are used in the experiments.

(Yuancheng Luo and Ramani Duraiswami, “Efficient Parallel Non-Negative Least Squares on Multicore Architectures”, SIAM Journal on Scientific Computing, accepted, Sep. 2011. [PDF] [Source code])