TY - JOUR
T1 - GPUML: Graphical processors for speeding up kernel machines
JF - Workshop on High Performance Analytics-Algorithms, Implementations, and Applications
Y1 - 2010
A1 - Srinivasan,B.V.
A1 - Hu,Q.
A1 - Duraiswami, Ramani
AB - Algorithms based on kernel methods play a central rolein statistical machine learning. At their core are a num- ber of linear algebra operations on matrices of kernel functions which take as arguments the training and test- ing data. These range from the simple matrix-vector product, to more complex matrix decompositions, and iterative formulations of these. Often the algorithms scale quadratically or cubically, both in memory and op- erational complexity, and as data sizes increase, kernel methods scale poorly. We use parallelized approaches on a multi-core graphical processor (GPU) to partially address this lack of scalability. GPUs are used to scale three different classes of problems, a simple kernel- matrix-vector product, iterative solution of linear sys- tems of kernel function and QR and Cholesky decom- position of kernel matrices. Application of these accel- erated approaches in scaling several kernel based learn- ing approaches are shown, and in each case substantial speedups are obtained. The core software is released as an open source package, GPUML.
ER -