Concurrent Parallel Distributed Computing (20 results)
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Hardback. Condition: New. Concurrent, Parallel, and Distributed Computing offers an excellent overview of the various areas of the computing field. There is a lot of overlap between the words "concurrent computing," "parallel computing," and "distributed computing," and there is no obvious differentiation between them. The same…system can be described as "parallel" and "distributed"; in a typical distributed system, the processors run concurrently in parallel. The content in the book is presented in such a way that even a reader with no prior knowledge of computers may understand it and become acquainted with the fundamental concepts of computing. It offers numerous small examples, demonstration materials, and sample exercises that teachers can use to teach parallel programming principles to students who have just recently been introduced to basic programming concepts. It focuses on Python multiprocessing features like fork/join threading, message passing, sharing resources between threads, and using locks. Parallelism's utility can be seen in applications like searching, sorting, and simulations. Students and researchers can get an accessible and comprehensive explanation of the concepts, guidelines, and, in particular, the complex instrumentation techniques used in computing.
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Hardback. Condition: New. Concurrent, Parallel, and Distributed Computing offers an excellent overview of the various areas of the computing field. There is a lot of overlap between the words "concurrent computing," "parallel computing," and "distributed computing," and there is no obvious differentiation between them. The same…system can be described as "parallel" and "distributed"; in a typical distributed system, the processors run concurrently in parallel. The content in the book is presented in such a way that even a reader with no prior knowledge of computers may understand it and become acquainted with the fundamental concepts of computing. It offers numerous small examples, demonstration materials, and sample exercises that teachers can use to teach parallel programming principles to students who have just recently been introduced to basic programming concepts. It focuses on Python multiprocessing features like fork/join threading, message passing, sharing resources between threads, and using locks. Parallelism's utility can be seen in applications like searching, sorting, and simulations. Students and researchers can get an accessible and comprehensive explanation of the concepts, guidelines, and, in particular, the complex instrumentation techniques used in computing.
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Unlock the Power of Parallel Python with Dask: A Perfect Learning Guide for Aspiring Data ScientistsDask has revolutionized parallel computing for Python, empowering data scientists to accelerate their workflows. This comprehensive guide… unravels the intricacies of Dask to help you harness its capabilities for machine learning and data analysis.Across 10 chapters, you'll master Dask's fundamentals, architecture, and integration with Python's scientific computing ecosystem. Step-by-step tutorials demonstrate parallel mapping, task scheduling, and leveraging Dask arrays for NumPy workloads. You'll discover how Dask seamlessly scales Pandas, Scikit-Learn, PyTorch, and other libraries for large datasets.Dedicated chapters explore scaling regression, classification, hyperparameter tuning, feature engineering, and more with clear examples. You'll also learn to tap into the power of GPUs with Dask, RAPIDS, and Google JAX for orders of magnitude speedups.This book places special emphasis on practical use cases related to scalability and distributed computing. You'll learn Dask patterns for cluster computing, managing resources efficiently, and robust data pipelines. The advanced chapters on DaskML and deep learning showcase how to build scalable models with PyTorch and TensorFlow.With this book, you'll gain practical skills to:Accelerate Python workloads with parallel mapping and task schedulingSpeed up NumPy, Pandas, Scikit-Learn, PyTorch, and other librariesBuild scalable machine learning pipelines for large datasetsLeverage GPUs efficiently via Dask, RAPIDS and JAXManage Dask clusters and workflows for distributed computingStreamline deep learning models with DaskML and DL frameworksPacked with hands-on examples and expert insights, this book provides the complete toolkit to harness Dask's capabilities. It will empower Python programmers, data scientists, and machine learning engineers to achieve faster workflows and operationalize parallel computing.Table of ContentIntroduction to DaskDask FundamentalsBatch Data Parallel Processing with DaskDistributed Systems and DaskAdvanced Dask: APIs and Building BlocksDask with PandasDask with Scikit-learnDask and PyTorchDask with GPUsScaling Machine Learning Projects with Dask.
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Taschenbuch. Condition: Neu. Distributed computing | Distributed algorithms, Concurrent computing, Parallel computing, Distributed memory, Parallel rendering, Distributed database, Distributed database management system | Frederic P. Miller (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130095550 | Verantwortli…che Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.
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Taschenbuch. Condition: Neu. Concurrent computing | Computing, Parallel computing, Central processing unit, Multiprocessing, Distributed computing, Process, (computing), Thread (computer science), Edsger W. Dijkstra, Per Brinch Hansen | Frederic P. Miller (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130213008… | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.
