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Rust Atomics and Locks

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by Mara Bos ...

The Rust programming language is extremely well suited for concurrency, and its ecosystem has many libraries that include lots of concurrent data structures, locks, and more. But implementing those structures correctly can be very difficult. Even in the most well-used libraries, memory ordering bugs are not uncommon.In this practical book, Mara Bos, leader of the Rust library team, helps Rust ... Read more

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The Rust programming language is extremely well suited for concurrency, and its ecosystem has many libraries that include lots of concurrent data structures, locks, and more. But implementing those structures correctly can be very difficult. Even in the most well-used libraries, memory ordering bugs are not uncommon.In this practical book, Mara Bos, leader of the Rust library team, helps Rust
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Shared Memory Application Programming

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Shared Memory Application Programming presents the key concepts and applications of parallel programming, in an accessible and engaging style applicable to developers across many domains. Multithreaded programming is today a core technology, at the basis of all software development projects in any branch of applied computer science. This book guides readers to develop insights about threaded
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Mastering Concurrency Programming with Java 9, Second Edition

Fast, reactive and parallel application development

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Master the principles to make applications robust, scalable and responsiveKey Features\[\*\] Implement concurrent applications using the Java 9 Concurrency API and its new components\[\*\] Improve the performance of your applications and process more data at the same time, taking advantage of all of your resources\[\*\] Construct real-world examples related to machine learning, data mining, ... Read more

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Master the principles to make applications robust, scalable and responsiveKey Features\[\*\] Implement concurrent applications using the Java 9 Concurrency API and its new components\[\*\] Improve the performance of your applications and process more data at the same time, taking advantage of all of your resources\[\*\] Construct real-world examples related to machine learning, data mining,
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Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book.Key FeaturesExpand your background in GPU programming—PyCUDA, scikit-cuda, and NsightEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolverApply GPU programming to modern data science ... Read more

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Practical GPU Programming

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Hadoop: The Definitive Guide

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Series series Chapman & Hall/CRC Computational Science
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Distributed Computing in Java 9

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Explore C++20 and C++23 features for efficient concurrent programming. Learn how to manage tasks, futures, and coroutines to write responsive and scalable C++ code. Enhance your applications with better control and executionKey FeaturesLearn how to use modern C++ features, including futures, promises, async, and coroutines to build asynchronous solutionsDevelop cross-platform network and low-level ... Read more

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Concurrent Programming in Mac OS X and iOS

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Distributed Systems with Elixir

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Embedded Computing for High Performance: Design Exploration and Customization Using High-level Compilation and Synthesis Tools provides a set of real-life example implementations that migrate traditional desktop systems to embedded systems. Working with popular hardware, including Xilinx and ARM, the book offers a comprehensive description of techniques for mapping computations expressed in
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Recommended For You

Parallel Programming eBooks

Rust Atomics and Locks

OpenCL Programming by Example

Shared Memory Application Programming

Mastering Concurrency Programming with Java 9, Second Edition

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Practical GPU Programming

Java 9 Concurrency Cookbook

Stream Processing with Apache Spark

Multithreading in C# 5.0 Cookbook

Learning Concurrency in Kotlin

Hadoop: The Definitive Guide

GPU Parallel Program Development Using CUDA

Fundamentals of Multicore Software Development

Parallel and High Performance Programming with Python

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GPU Programming with C++ and CUDA

Parallel Programming with Python

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Asynchronous Programming with C++

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