Prediction of the Running Time for Multiplying Two 2048x2048 Matrices Using a GPU OpenCL SGEMM Kernel

Predicting execution time helps optimize performance, allocate resources, and compare hardware efficiently. For a fundamental operation like matrix multiplication, the applications span across various fields like high-performance computing and cryptography, and it would greatly benefit from accurate predictions. This paper presents the implementation of various models that predict the computation time of a system consisting of a CPU (Central Processing Unit) as well as a GPU (Graphics Processing Unit) as it performs the multiplication of two 2048 x 2048 matrices. The prediction is based on GPU specifications, which consist of 14 different configurations. The prediction models were able to properly learn the data and generalize exceptionally on new unseen data with a prediction accuracy of 99.94% exceeding the results of previous literature.

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Published
2025-04-16
DOI
https://doi.org/10.1109/ictcs65341.2025.10989440
Primary Topic
Neural Networks and Applications
Type
article
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article

Prediction of the Running Time for Multiplying Two 2048x2048 Matrices Using a GPU OpenCL SGEMM Kernel

Heba Abdel-Nabi, Mariam Mourad, Qabas Alkaissi, Mahmoud Abu-Qtiesh
Neural Networks and Applications
article

Prediction of the Running Time for Multiplying Two 2048x2048 Matrices Using a GPU OpenCL SGEMM Kernel

Heba Abdel-Nabi, Mariam Mourad, Qabas Alkaissi, Mahmoud Abu-Qtiesh
article en

Abstract

Predicting execution time helps optimize performance, allocate resources, and compare hardware efficiently. For a fundamental operation like matrix multiplication, the applications span across various fields like high-performance computing and cryptography, and it would greatly benefit from accurate predictions. This paper presents the implementation of various models that predict the computation time of a system consisting of a CPU (Central Processing Unit) as well as a GPU (Graphics Processing Unit) as it performs the multiplication of two 2048 x 2048 matrices. The prediction is based on GPU specifications, which consist of 14 different configurations. The prediction models were able to properly learn the data and generalize exceptionally on new unseen data with a prediction accuracy of 99.94% exceeding the results of previous literature.

Princess Sumaya University for Technology (JO)
Affordable and clean energy
Openalex Percentile: Top 95%
Neural Networks and Applications
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