Project details for GPUML GPUs for kernel machines

Logo GPUML GPUs for kernel machines 4

by balajivasan - February 26, 2010, 18:12:46 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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Description:

Kernel machines are integral part of many learning approaches. We have identified 3 key computations encountered in these algorithms. 1. Weighted summation of kernel function (ex. kernel density estimation) 2. Kernel matrix-vector product in an iterative algorithm (ex. kernel regression) 3. Operations on the kernel matrix (ex. kernel PCA)

In this software, we accelerate each of these on a GPU. See the documentation for more details, and demo to see the speedups.

System requirement: Cuda 2.2+, Visual studio 2008 (if used in Windows)

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Linux, Windows
Data Formats: None
Tags: Kernel Methods
Archive: download here

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