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Profiling JVM for AI Applications Using Deep Learning Libraries

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 141)

Abstract

In contemporary times, artificial intelligence (AI) techniques are widely used in development of software applications. Most of the AI-based applications are designed to perform complex tasks such as medical diagnosis, data analytic, human assistants, etc. The performance of such applications depends on development environment. Virtual machines are commonly being used for such development environments. These act as middlewares that support necessary tools for execution of applications. Java Virtual Machine (JVM) is one of the popular virtual environment which is used for several applications. It provides a secure, flexible, and fast execution environment. Therefore, we choose JVM to explore its suitability for AI applications. In this paper, we analyze JVM performance for different AI applications which include deep learning libraries. We use a profiling tool visualVM which profiles JVM performance for running applications. Our goal is to explore key strengths of JVM for AI applications. This in-depth analysis of JVM may help the developer community to choose an appropriate environment for AI applications development.

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Correspondence to Neha Kumari .

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Kumari, N., Kumar, R. (2021). Profiling JVM for AI Applications Using Deep Learning Libraries. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_39

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