An Empirical Evaluation of AriDeM using Matrix Multiplication

TL;DR

This paper empirically evaluates AriDeM, a new parallel computation model, demonstrating its efficiency in resource utilization compared to the traditional von Neumann model through matrix multiplication experiments.

Contribution

It provides the first empirical assessment of AriDeM, validating its theoretical advantages over von Neumann in parallel computing contexts.

Findings

AriDeM outperforms von Neumann in resource efficiency.

Empirical results align with theoretical predictions.

AriDeM shows promise for high-performance parallel computing.

Abstract

For a long time, the Von Neumann has been a successful model of computation for sequential computing .Many models including the dataflow model have been unsuccessfully developed to emulate the same results in parallel computing. It is widely accepted that high performance computation is better-achieved using parallel architectures and is seen as the basis for future computational architectures with the ever-increasing need for high performance computation. We describe a new model of parallel computation known as the Arithmetic Deduction Model (AriDem) which has some similarities with the Von Neumann. A theoretical evaluation conducted on this model in comparison with the predominant von Neumann model indicated AriDeM to be more efficient in resources utilization. In this paper, we conduct an empirical evaluation of the model and the results reflect the output of the theoretical evaluation.