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| Paper: |
CUDA-Accelerated SVM for Celestial Object Classification |
| Volume: |
442, Astronomical Data Analysis Software and Systems XX (ADASSXX) |
| Page: |
119 |
| Authors: |
Peng, N.; Zhang, Y.; Zhao, Y. |
| Abstract: |
Recently, the development in highly parallel Graphics Processing
Units (GPUs) provides us a new method to solve advanced computation
problems. We introduce an automated method called Support Vector
Machine (SVM) based on Nvidia's Compute Unified Device Architecture (CUDA) platform for classifying celestial objects. SVM has been
proved a good algorithm for separating quasars from stars, but it
takes a lot of time for training and predicting with large samples.
Using the data adopted from the Sloan Digital Sky Survey (SDSS) Data
Release Seven (DR7), CUDA-accelerated SVM shows achieving greatly
improved speedups over commonly used SVM software running on a CPU.
It achieves speedups of 1.25–9.96× in training and 9.29–364.4×
in predicting. This approach is effective and applicable for quasar
selection in order to compile an input catalog for the Large Sky
Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). |
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