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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

GPU acceleration in Java – leveraging CUDA, OpenCL, and native libraries

To harness the immense computational power of GPUs within Java applications, developers have several options at their disposal. This section explores how Java developers can leverage CUDA, OpenCL, and native libraries to accelerate computations and tap into the parallel processing capabilities of GPUs. We’ll delve into the strengths and weaknesses of each approach, guiding you toward the most suitable solution for your specific use case.

Fundamentals of GPU computing

GPUs have evolved from their original purpose of rendering graphics to becoming powerful tools for general-purpose computation. This shift, known as general-purpose computing on graphics processing units (GPGPU), leverages the parallel processing capabilities of GPUs to perform computations more efficiently than traditional CPUs in certain tasks.

Unlike CPUs, which have a few cores optimized for sequential processing, GPUs have...

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