Performance and Energy Consumption of Parallel Machine Learning Algorithms

TL;DR

This paper evaluates the performance and energy consumption of parallel machine learning algorithms, including logistic regression, genetic algorithms, and neural networks, across different parallelization methods and data sizes.

Contribution

It provides a comparative analysis of power and performance impacts of various parallelization techniques on machine learning training algorithms.

Findings

Parallel efficiency varies with model complexity and data size.

GPU acceleration significantly speeds up training.

Power consumption is influenced by parallelization method and data scale.

Abstract

Machine learning models have achieved remarkable success in various real-world applications such as data science, computer vision, and natural language processing. However, model training in machine learning requires large-scale data sets and multiple iterations before it can work properly. Parallelization of training algorithms is a common strategy to speed up the process of training. However, many studies on model training and inference focus only on aspects of performance. Power consumption is also an important metric for any type of computation, especially high-performance applications. Machine learning algorithms that can be used on low-power platforms such as sensors and mobile devices have been researched, but less power optimization is done for algorithms designed for high-performance computing. In this paper, we present a C++ implementation of logistic regression and the genetic algorithm, and a Python implementation of neural networks with stochastic gradient descent (SGD) algorithm on classification tasks. We will show the impact that the complexity of the model and the size of the training data have on the parallel efficiency of the algorithm in terms of both power and performance. We also tested these implementations using shard-memory parallelism, distributed memory parallelism, and GPU acceleration to speed up machine learning model training.