2011 AGPUTailoredApproachforTraining

• (Cotter et al., 2011) ⇒ Andrew Cotter, Nathan Srebro, and Joseph Keshet. (2011). “A GPU-tailored Approach for Training Kernelized SVMs.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020548

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+A+GPU-tailored+Approach+for+Training+Kernelized+SVMs
• http://dl.acm.org/citation.cfm?id=2020408.2020548&preflayout=flat#citedby

Quotes

Author Keywords

• Concurrent programming; design; experimentation; gpgpu; performance

Abstract

We present a method for efficiently training binary and multiclass kernelized SVMs on a Graphics Processing Unit (GPU). Our methods apply to a broad range of kernels, including the popular Gaussian kernel, on datasets as large as the amount of available memory on the graphics card. Our approach is distinguished from earlier work in that it cleanly and efficiently handles sparse datasets through the use of a novel clustering technique. Our optimization algorithm is also specifically designed to take advantage of the graphics hardware. This leads to different algorithmic choices then those preferred in serial implementations. Our easy-to-use library is orders of magnitude faster then existing CPU libraries, and several times faster than prior GPU approaches.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 AGPUTailoredApproachforTraining	Andrew Cotter Nathan Srebro Joseph Keshet			A GPU-tailored Approach for Training Kernelized SVMs				10.1145/2020408.2020548		2011