%0 Journal Article
%J Workshop on High Performance Analytics-Algorithms, Implementations, and Applications
%D 2010
%T GPUML: Graphical processors for speeding up kernel machines
%A Srinivasan,B.V.
%A Hu,Q.
%A Duraiswami, Ramani
%X 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.
%B Workshop on High Performance Analytics-Algorithms, Implementations, and Applications
%8 2010///
%G eng