Technique detection software for Sparse Matrices

TL;DR

This paper discusses a technique detection software designed to identify the most suitable sparse matrix storage format, optimizing storage size and processing efficiency based on matrix data distribution.

Contribution

It introduces a method to select optimal sparse matrix storage formats tailored to data distribution, enhancing performance and storage efficiency.

Findings

Improved selection of storage formats for sparse matrices.

Enhanced processing efficiency and reduced storage size.

Guidelines for choosing formats based on matrix distribution.

Abstract

Sparse storage formats are techniques for storing and processing the sparse matrix data efficiently. The performance of these storage formats depend upon the distribution of non-zeros, within the matrix in different dimensions. In order to have better results we need a technique that suits best the organization of data in a particular matrix. So the decision of selecting a better technique is the main step towards improving the system's results otherwise the efficiency can be decreased. The purpose of this research is to help identify the best storage format in case of reduced storage size and high processing efficiency for a sparse matrix.