Rinnakkainen käsittely
Parallelliprosessointi tutkii, miten useita prosessoreita tai ydinprosessoriyksiköitä voi käyttää samanaikaisesti tehtävien suorittamiseen. Tämä aihe on keskeinen modernissa tietojenkäsittelytieteessä ja se tarjoaa syvällisiä näkemyksiä tehokkuuden parantamisesta ja suorituskyvyn lisäämisestä. Koska monimutkaiset laskentatehtävät kasvavat, parallelliprosessoinnin ymmärtäminen on elintärkeää opiskelijoille ja ammattilaisille, jotka haluavat pysyä ajan tasalla alan kehityksestä.
Programming Rust: Fast, Safe Systems Development
Leonora F. S. Tindall, Jason Orendorff, Jim Blandy
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Parallel and High Performance Programming with Python: Unlock parallel and concurrent programming in Python using multithreading, CUDA, Pytorch and Dask.
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Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith
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Observability Engineering: Achieving Production Excellence
George Miranda, Charity Majors, Liz Fong-Jones
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Specifying Systems: The TLA+ Language and Tools for Hardware and Software Engineers: The TLA+ Language and Tools for Hardware and Software Engineers
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Functional and Concurrent Programming: Core Concepts and Features
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Programming Massively Parallel Processors: A Hands-on Approach
Izzat El Hajj, Wen-Mei W. Hwu, David B. Kirk
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Cuda by Example: An Introduction to General-Purpose Gpu Programming
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Specifying Systems: The TLA+ Language and Tools for Hardware and Software Engineers: The TLA+ Language and Tools for Hardware and Software Engineers
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Python Parallel Programming Cookbook
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Programming Models for Parallel Computing
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Mastering C# Concurrency
Andrew Koryavchenko, Eugene Agafonov
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Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
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Formal and Adaptive Methods for Automation of Parallel Programs Construction: Emerging Research and Opportunities
Anatoliy Doroshenko, Olena Yatsenko
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Formal and Adaptive Methods for Automation of Parallel Programs Construction: Emerging Research and Opportunities
Anatoliy Doroshenko, Olena Yatsenko
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iPhone 6s Plus: The Ultimate Guide to Revolutionizing Your iPhone Mobile
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Multicore and GPU Programming: An Integrated Approach
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Implementing Parallel and Distributed Systems
M. B. Ghaznavi-Ghoushchi, Alireza Poshtkohi
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Rust In Practice: A Programmers Guide to Build Rust Programs, Test Applications and Create Cargo Packages
Ralph J, Rustacean Team, B. Anderson
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Practical Rust 1.x Cookbook: 100+ Solutions across Command Line, CI/CD, Kubernetes, Networking, Code Performance and Microservices
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Parallel Python with Dask: Perform distributed computing, concurrent programming and manage large dataset
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Diving Into Parallel Universes: Reality Reimagined
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Optimizing Compilers for Modern Architectures: A Dependence-Based Approach
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The Sourcebook of Parallel Computing
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The Grid 2: Blueprint for a New Computing Infrastructure
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Luokka "Rinnakkainen käsittely"
Parallel processing on a computing level represents a significant evolution in how tasks are handled, allowing multiple operations to happen simultaneously. This category is designed for anyone interested in enhancing their understanding of computer architecture and logic design, whether they are students, educators, or seasoned professionals in the field of computer science.
The concept dates back to the early days of computing but has gained substantial momentum with the advent of multi-core processors and advanced algorithms. By exploring the principles of parallel processing, readers can uncover the potential to optimize performance in various applications, from high-performance computing to data-intensive workloads.
Books within this category delve into crucial topics such as task scheduling, workload distribution, and the architectural designs that enable efficient parallelism. Understanding these principles is pivotal for anyone looking to stay competitive in an ever-evolving technological landscape.
Engaging with works from renowned authors and publishers in this discipline not only sheds light on the technical aspects but also offers insights into groundbreaking research and practical implementations. Parallel processing is not merely an academic subject; it represents real-world applications that contribute to innovations across industries, making this knowledge invaluable for future developments in computing.