  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.
