Astrophysical data mining with GPU. A case study: genetic classification of globular clusters by Stefano Cavuoti, Mauro Garofalo, Massimo Brescia, Maurizio Paolillo, Antonio Pescape’, Giuseppe Longo, Giorgio Ventre.
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), 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 200x in the training phase with respect to the CPU based version.
BTW, DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html.
In case you are curious about the application of genetic algorithms in a low signal/noise situation with really “big” data, this is a good starting point.
Makes me curious about the “noise” in other communications.
The “signal” is fairly easy to identify in astronomy, but what about in text or speech?
I suppose “background noise, music, automobiles” would count as “noise” on a tape recording of a conversation, but is there “noise” in a written text?
Or noise in a conversation that is clearly audible?
If we have 100% signal, how do we explain failing to understand a message in speech or writing?
If it is not “noise,” then what is the problem?