Programming Language Selection for The Development of Deep Learning Library

Oktavia Rachmawati - Electronic Engineering Polytechnic Institute of Surabaya, Surabaya, 60111, Indonesia
Ali Barakbah - Electronic Engineering Polytechnic Institute of Surabaya, Surabaya, 60111, Indonesia
Tita Karlita - Electronic Engineering Polytechnic Institute of Surabaya, Surabaya, 60111, Indonesia

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Recently, deep learning has become very successful in various applications, leading to an increasing need for software tools to keep up with the rapid pace of innovation in deep learning research. As a result, we suggested the development of a software library related to deep learning that would be useful for researchers and practitioners in academia and industry for their research endeavors. The programming language is the core of deep learning library development, so this paper describes the selection stage to find the most suitable programming language for developing a deep learning library based on two criteria, including coverage on many projects and the ability to handle high-dimensional array processing. We addressed the comparison of programming languages with two approaches. First, we looked for the most demanding programming languages for AI Jobs by conducting a data-driven approach against the data gathered from several Job-Hunting Platforms. Then, we found the findings that imply Python, C++, and Java as the top three. After that, we compared the three most widely used programming languages by calculating interval time to three different programs that contain an array of exploitation processes. Based on the result of the experiments that were executed in the computer terminal, Java outperformed Python and C++ in two of the three experiments conducted with 5,4047 milliseconds faster than C++ and 231,1639 milliseconds faster than Python to run quick sort algorithm for arrays that contain 100.000 integer values.



Programming Language; Data-Driven Approach; Software Development Life Cycle; Software Library; Deep Learning

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