An Analytical Evaluation of Matricizing Least-Square-Errors Curve Fitting to Support High Performance Computation on Large Datasets

TL;DR

This paper introduces a matricized approach to Least Square-Errors curve fitting designed to enable parallel processing, significantly improving efficiency for large datasets in high-performance computing environments.

Contribution

It proposes a novel matricized method for Least Square-Errors curve fitting that facilitates parallelization and enhances computational efficiency on large datasets.

Findings

The matricized approach allows effective parallelization.

Significant speed-up in curve fitting on large datasets.

Potential for high-performance computation applications.

Abstract

The procedure of Least Square-Errors curve fitting is extensively used in many computer applications for fitting a polynomial curve of a given degree to approximate a set of data. Although various methodologies exist to carry out curve fitting on data, most of them have shortcomings with respect to efficiency especially where huge datasets are involved. This paper proposes and analyzes a matricized approach to the Least Square-Errors curve fitting with the primary objective of parallelizing the whole algorithm so that high performance efficiency can be achieved when algorithmic execution takes place on colossal datasets.