Benchmarking Machine Learning: How Fast Can Your Algorithms Go?

TL;DR

This paper evaluates various techniques to accelerate machine learning algorithms, including vector caches and parallel execution, through review and experimental results to understand their effectiveness.

Contribution

It provides a comparative analysis of different speed-up techniques in machine learning, combining review and new experimental insights.

Findings

Vector caches improve speed in certain algorithms

Parallel execution significantly reduces training time

Experimental results highlight the most effective techniques

Abstract

This paper is focused on evaluating the effect of some different techniques in machine learning speed-up, including vector caches, parallel execution, and so on. The following content will include some review of the previous approaches and our own experimental results.