SVD BASED LATENT SEMANTIC INDEXING WITH USE OF THE GPU COMPUTATIONS

The purpose of this article is to determine the usefulness of the Graphics Processing Unit (GPU) calculations used to implement the Latent Semantic Indexing (LSI) reduction of the TERM-BYDOCUMENT matrix. Considered reduction of the matrix is based on the use of the SVD (Singular Value Decomposition) decomposition. A high computational complexity of the SVD decomposition - O(n3 ), causes that a reduction of a large indexing structure is a difficult task. In this article there is a comparison of the time complexity and accuracy of the algorithms implemented for two different environments. The first environment is associated with the CPU and MATLAB R2011a. The second environment is related to graphics processors and the CULA library. The calculations were carried out on generally available benchmark matrices, which were combined to achieve the resulting matrix of high size. For both considered environments computations were performed for double and single precision data.Nowadays, there is a tremendous increase in the number of text resources associated with various themes. The most obvious example of this phenomenon is still growing the World Wide Web. With the increase in the number of text documents placed in various databases increasingly important are the methods of automatic documents indexing. The LSI method, introduced in 1990, uses the theory of linear algebra to automate the process of indexing and retrieval