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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

Questions

  1. What is the primary benefit of integrating Java’s concurrency mechanisms into ML workflows?
    1. To increase the programming complexity
    2. To enhance data security
    3. To optimize computational efficiency
    4. To simplify code documentation
  2. Which Java tool is highlighted as crucial for processing large datasets in ML projects quickly?
    1. Java Database Connectivity (JDBC)
    2. Java Virtual Machine (JVM)
    3. Parallel Streams
    4. JavaFX
  3. What role do custom thread pools play in Java concurrency for ML?
    1. They decrease the performance of ML models.
    2. They are used to manage database transactions only.
    3. They improve scalability and manage large-scale computations.
    4. They simplify the user interface design.
  4. Which of the following is a suggested application of Java’s concurrency in ML as discussed in this chapter?
    1. To handle multiple user interfaces simultaneously
    2. To perform data preprocessing and model training more efficiently
    3. To replace Python in scientific computing
    4. To manage client-server architecture...

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