Name
Abstract | ||
|---|---|---|
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-35467-0_4 | WIRN |
Field | DocType | Volume |
Computer science,CUDA,Parallel computing,Genetic programming,Exploit,General-purpose computing on graphics processing units,Web application,Data classification,Genetic algorithm,Scalability | Journal | abs/1211.5481 |
Citations | PageRank | References |
4 | 0.41 | 1 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stefano Cavuoti | 1 | 10 | 4.79 |
| Mauro Garofalo | 2 | 11 | 3.72 |
| Massimo Brescia | 3 | 14 | 8.41 |
| Antonio Pescapè | 4 | 1076 | 87.91 |
| Giuseppe Longo | 5 | 78 | 16.22 |
| Giorgio Ventre | 6 | 525 | 61.20 |