Multiplication of thin algorithmic matrices is a simple but very important part of linear and scientific algebra programs in mathematics and physics, and due to its parallel nature, GPUs are one of the most suitable and important options. To select its executive platfor More
Multiplication of thin algorithmic matrices is a simple but very important part of linear and scientific algebra programs in mathematics and physics, and due to its parallel nature, GPUs are one of the most suitable and important options. To select its executive platform. In recent years, due to the emphasis of researchers to consider energy consumption as one of the main design goals along with efficiency, very little effort has been made to improve the energy consumption of this algorithm on the GPU. In this article, this issue is addressed from the perspective of energy efficiency in efficiency obtained.
Utilizing the configuration capability introduced in modern GPUs, by statistically examining the behavior of this algorithm when using different thin matrix storage formats and different hardware settings for more than 200 matrices
Slim example, the best configuration settings for the thin matrix multiplication algorithm with different storage formats on the GPU are obtained. This configuration for each storage format is selected to give the best configuration in all samples tested.
Manuscript profile
