Parallelization and Characterization of Probabilistic Latent Semantic Analysis

Abstract

Probabilistic Latent Semantic Analysis (PLSA) is one of the most popular statistical techniques for the analysis of two-model and co-occurrence data. It has applications in information retrieval and filtering, nature language processing, machine learning from text, and other related areas. However, PLSA is rarely applied to large datasets due to its high computational complexity.This paper presents an optimized and parallelized implementation of PLSA which is capable of processing datasets with 10000 documents in seconds. Compared to the baseline program, our parallelized program can achieve speedup of more than six on an eight-processor machine. The characterization of the parallel program is also presented. The performance analysis of the parallel program indicates that this program is memory intensive and the limited memory bandwidth is the bottleneck for better speedup.