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Java Concurrency and Parallelism

Java Concurrency and Parallelism

By : Jay Wang
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Java Concurrency and Parallelism

Java Concurrency and Parallelism

5 (1)
By: Jay Wang

Overview of this book

If you’re a software developer, architect, or systems engineer, exploring Java’s concurrency utilities and synchronization in the cloud, this book is an essential resource. Tech visionary Jay Wang, with over three decades of experience transforming industry giants, brings unparalleled expertise to guide you through Java’s concurrency and parallel processing in cloud computing. This comprehensive book starts by establishing the foundational concepts of concurrency and parallelism, vital for cloud-native development, and gives you a complete overview, highlighting challenges and best practices. Wang expertly demonstrates Java’s role in big data, machine learning, microservices, and serverless computing, shedding light on how Java’s tools are effectively utilized in these domains. Complete with practical examples and insights, this book bridges theory with real-world applications, ensuring a holistic understanding of Java in cloud-based scenarios. You’ll navigate advanced topics, such as synchronizing Java’s concurrency with cloud auto-scaling and GPU computing, and be equipped with the skills and foresight to tackle upcoming trends in cloud technology. This book serves as your roadmap to innovation and excellence in Java cloud applications, giving you in-depth knowledge and hands-on practice for mastering Java in the cloud era.
Table of Contents (20 chapters)
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Part 1: Foundations of Java Concurrency and Parallelism in Cloud Computing
7
Part 2: Java's Concurrency in Specialized Domains
12
Part 3: Mastering Concurrency in the Cloud – The Final Frontier

Summary

This chapter offered an in-depth exploration of harnessing Java’s concurrency mechanisms to significantly enhance ML processes. By facilitating the simultaneous execution of multiple operations, Java effectively shortens the durations required for data preprocessing and model training, which are critical bottlenecks in ML workflows. The chapter presented practical examples and case studies that demonstrate how Java’s concurrency capabilities can be applied to real-world ML applications. These examples vividly showcased the substantial improvements in performance and scalability that could be achieved.

Furthermore, the chapter outlined specific strategies, such as utilizing parallel streams and custom thread pools, to optimize large-scale data processing and perform complex computations efficiently. This discussion is crucial for developers aiming to enhance the scalability and performance of ML systems. Additionally, the text provided a detailed list of necessary...

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