Nowadays, the Machine Learning (ML) approach is needful to many research fields. Among these, the Environmental Science (ES) which involves a large amount of data to be processed and collected. On the other hand, in order to provide a reliable output, those data information must be assimilated. Since this process requires a large execution time when the input dataset is very huge, here we propose a parallel GPU algorithm based on a curve fitting method, to filter the starting dataset, by exploiting the computational power of the CUDA tool. The innovative aspect of the proposed procedure can be used in several application fields. Our experiments show the achieved results in terms of performance.
A GPU-Based Algorithm for Environmental Data Filtering
De Luca P.;Galletti A.;Marcellino L.
2022-01-01
Abstract
Nowadays, the Machine Learning (ML) approach is needful to many research fields. Among these, the Environmental Science (ES) which involves a large amount of data to be processed and collected. On the other hand, in order to provide a reliable output, those data information must be assimilated. Since this process requires a large execution time when the input dataset is very huge, here we propose a parallel GPU algorithm based on a curve fitting method, to filter the starting dataset, by exploiting the computational power of the CUDA tool. The innovative aspect of the proposed procedure can be used in several application fields. Our experiments show the achieved results in terms of performance.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
