Enhancing Java Virtual Machine Performance for Scalable Artificial Intelligence and Machine Learning Workloads

Abstract

The increasing workloads on AI and ML, at scale, have revealed some of the largest performance limitations in the Java Virtual Machine (JVM), which is a popular runtime platform used in Java applications. The paper explores the problems and opportunities of improving the performance of the JVM in the case of resource-intensive AI/ML applications. The paper discusses the weaknesses of JVM in terms of memory management, garbage collection, and parallelism that limit its effectiveness in large machine learning models and real-time data processing. By utilizing a mixed-methods research method, the article analyzes various optimization strategies, including memory allocation, concurrency models, and hardware integration. A case study on performance benchmarking reveals significant improvements in processing speed, memory efficiency, and scalability, all of which are targeted JVM enhancements. The comparative analysis of native and GPU-implemented frameworks highlights the potential of the JVM in AI/ML-based applications, identifying possible areas for future exploration. The paper is finalized with practical recommendations on how JVM performance can be optimized in AI/ML settings, and what directions may be pursued further in that regard.