Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

# Vedic Mathematics Approach to Speedup Parallel Computations

Version 1 : Received: 26 February 2022 / Approved: 2 March 2022 / Online: 2 March 2022 (07:37:16 CET)

How to cite: Shrawankar, U.; Shrawankar, C. Vedic Mathematics Approach to Speedup Parallel Computations. Preprints 2022, 2022030036. https://doi.org/10.20944/preprints202203.0036.v1 Shrawankar, U.; Shrawankar, C. Vedic Mathematics Approach to Speedup Parallel Computations. Preprints 2022, 2022030036. https://doi.org/10.20944/preprints202203.0036.v1

## Abstract

Solving Linear equations with large number of variable contains many computations to be performed either iteratively or recursively. Thus it consumes more time when implemented in a sequential manner. There are many ways to solve the linear equations such as Gaussian elimination, Cholesky factorization, LU factorization, QR factorization. But even these methods when implemented on a sequential platform yield slower results as compared to a parallel platform where the time consumption is reduced considerably due to concurrent execution of instructions. The above mentioned linear equation solving methods can be implemented on the parallel platform using the direct approaches such as pipelining or 1D and 2D Partitioning approach. Vedic mathematics is a very ancient approach for solving mathematical problems. These Vedic mathematical approaches are well known for quicker and faster computation of mathematical problems. Vedic Mathematics provides a very different outlook towards the approach of solving linear Equation on parallel platform. It could be considered as a better approach for reducing space consumption and minimizing the number of algebraic operations involved in solving linear equation. In future Vedic Mathematics might serve as a viable solution for solving linear equation on parallel platform.

## Keywords

Vedic mathematics; parallel computation; parallelism; Multicore Systems; pipelining; 1D and 2D partitioning; linear equations solving; Paravartya Yojayet method; Sunyam Anyat method; Sankalana Vyavakalanabhyam method; Sopantyadvayamantyam method

## Subject

Computer Science and Mathematics, Computational Mathematics