The scheduling based on machine learning for heterogeneous CPU/GPU systems

Abstract

Efficient use all of the available computing devices is an important issue for heterogeneous computing systems. The ability to choose a CPU or GPU processor for a specific task has a positive impact on the performance of GPGPU-systems. It helps to reduce the total processing time and to achieve the uniform system utilization. In this paper, we propose a scheduler that selects the executing device after prior training, based on the size of the input data. The article also contains the plots and time characteristics that demonstrate improvement in overall execution time, depending on the input data. The program modules were developed in C++ using CUDA libraries.