A Processor Selection Method based on Execution Time Estimation for Machine Learning Programs

In recent years, machine learning has become widespread. Since machine learning algorithms have become complex and the amount of data to be handled have become large, the execution times of machine learning programs have been increasing. Processors called accelerators can contribute to the execution of a machine learning program with a short time. However, the processors including the accelerators have different characteristics. Therefore, it is unclear whether existing machine learning programs are executed on the appropriate processor or not. This paper proposes a method for selecting a processor suitable for each machine learning program. In the proposed method, the selection is based on the estimation of the execution time of machine learning programs on each processor. The proposed method does not need to execute a target machine learning program in advance. From the experimental results, it is clarified that the proposed method can achieve up to 5.3 times faster execution than the original implementation by NumPy. These results prove that the proposed method can be used in a system that automatically selects the processor so that each machine learning program can be easily executed on the best processor.