@article {13958,
	title = {GPUML: Graphical processors for speeding up kernel machines},
	journal = {Workshop on High Performance Analytics-Algorithms, Implementations, and Applications},
	year = {2010},
	month = {2010///},
	abstract = {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.
},
	author = {Srinivasan,B.V. and Hu,Q. and Duraiswami, Ramani}
}
