© 2017 Gabriel A. León-Paredes et al. Latent Semantic Analysis (LSA) is a method that allows us to automatically index and retrieve information from a set of objects by reducing the term-by-document matrix using the Singular Value Decomposition (SVD) technique. However, LSA has a high computational cost for analyzing large amounts of information. The goals of this work are (i) to improve the execution time of semantic space construction, dimensionality reduction, and information retrieval stages of LSA based on heterogeneous systems and (ii) to evaluate the accuracy and recall of the information retrieval stage.We present a heterogeneous Latent Semantic Analysis (hLSA) system,which has been developed usingGeneral-Purpose computing onGraphics ProcessingUnits (GPGPUs) architecture, which can solve large numeric problems faster through the thousands of concurrent threads onmultiple CUDA cores ofGPUs and multi-CPU architecture, which can solve large text problems faster through a multiprocessing environment.We execute the hLSA system with documents from the PubMed Central (PMC) database. The results of the experiments show that the acceleration reached by the hLSA system for largematrices with one hundred and fifty thousand million values is around eight times faster than the standard LSA version with an accuracy of 88% and a recall of 100%.
|Título traducido de la contribución||Un sistema heterogéneo basado en el análisis semántico latente mediante GPU y multi-CPU.|
|Estado||Publicada - 1 ene. 2017|