Astrophysical data mining with GPU. A case study: Genetic classification of globular clusters

Journal
Date Issued
2014
Author(s)
•
Garofalo, M.
•
•
•
Pescape', A.
•
Longo, G.
•
Ventre, G.
DOI
Abstract
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200× in the training phase with respect to the CPU based version.
Volume
26
Start page
12
Uri
Url
Issn Identifier
1384-1076
Ads BibCode
2014NewA...26...12C
Rights
open.access
File(s)
Loading...
Thumbnail Image
Name

1304.0597.pdf

Description
postprint
Size

461.02 KB

Format

Adobe PDF

Checksum (MD5)

996a16b3107ff658c05beae0dfb7e207

Loading...
Thumbnail Image
Name

New astr. 26_2014.pdf

Description
[Administrators only]
Size

1.21 MB

Format

Adobe PDF

Checksum (MD5)

d75bbfd249deac3b939ffa193b127580