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Author: Francesco Pierfederici
Publisher: Packt Publishing Ltd
ISBN: 1785887041
Category : Computers
Languages : en
Pages : 171
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Book Description
Harness the power of multiple computers using Python through this fast-paced informative guide About This Book You'll learn to write data processing programs in Python that are highly available, reliable, and fault tolerant Make use of Amazon Web Services along with Python to establish a powerful remote computation system Train Python to handle data-intensive and resource hungry applications Who This Book Is For This book is for Python developers who have developed Python programs for data processing and now want to learn how to write fast, efficient programs that perform CPU-intensive data processing tasks. What You Will Learn Get an introduction to parallel and distributed computing See synchronous and asynchronous programming Explore parallelism in Python Distributed application with Celery Python in the Cloud Python on an HPC cluster Test and debug distributed applications In Detail CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications. This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more. Style and Approach This example based, step-by-step guide will show you how to make the best of your hardware configuration using Python for distributing applications.
Author: Francesco Pierfederici
Publisher: Packt Publishing Ltd
ISBN: 1785887041
Category : Computers
Languages : en
Pages : 171
Get Book
Book Description
Harness the power of multiple computers using Python through this fast-paced informative guide About This Book You'll learn to write data processing programs in Python that are highly available, reliable, and fault tolerant Make use of Amazon Web Services along with Python to establish a powerful remote computation system Train Python to handle data-intensive and resource hungry applications Who This Book Is For This book is for Python developers who have developed Python programs for data processing and now want to learn how to write fast, efficient programs that perform CPU-intensive data processing tasks. What You Will Learn Get an introduction to parallel and distributed computing See synchronous and asynchronous programming Explore parallelism in Python Distributed application with Celery Python in the Cloud Python on an HPC cluster Test and debug distributed applications In Detail CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications. This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more. Style and Approach This example based, step-by-step guide will show you how to make the best of your hardware configuration using Python for distributing applications.
Author: Tim Peters
Publisher: GitforGits
ISBN: 8119177460
Category : Computers
Languages : en
Pages : 172
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Book Description
Unlock the Power of Parallel Python with Dask: A Perfect Learning Guide for Aspiring Data Scientists Dask 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 scheduling Speed up NumPy, Pandas, Scikit-Learn, PyTorch, and other libraries Build scalable machine learning pipelines for large datasets Leverage GPUs efficiently via Dask, RAPIDS and JAX Manage Dask clusters and workflows for distributed computing Streamline deep learning models with DaskML and DL frameworks Packed 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 Content Introduction to Dask Dask Fundamentals Batch Data Parallel Processing with Dask Distributed Systems and Dask Advanced Dask: APIs and Building Blocks Dask with Pandas Dask with Scikit-learn Dask and PyTorch Dask with GPUs Scaling Machine Learning Projects with Dask
Author: Vijay K. Garg
Publisher: John Wiley & Sons
ISBN: 0471721263
Category : Computers
Languages : en
Pages : 331
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Book Description
Concurrent and Distributed Computing in Java addresses fundamental concepts in concurrent computing with Java examples. The book consists of two parts. The first part deals with techniques for programming in shared-memory based systems. The book covers concepts in Java such as threads, synchronized methods, waits, and notify to expose students to basic concepts for multi-threaded programming. It also includes algorithms for mutual exclusion, consensus, atomic objects, and wait-free data structures. The second part of the book deals with programming in a message-passing system. This part covers resource allocation problems, logical clocks, global property detection, leader election, message ordering, agreement algorithms, checkpointing, and message logging. Primarily a textbook for upper-level undergraduates and graduate students, this thorough treatment will also be of interest to professional programmers.
Author: Vijay K. Garg
Publisher: John Wiley & Sons
ISBN: 9780471036005
Category : Computers
Languages : en
Pages : 448
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Book Description
Mit der Verfügbarkeit verteilter Systeme wächst der Bedarf an einer fundamentalen Diskussion dieses Gebiets. Hier ist sie! Abgedeckt werden die grundlegenden Konzepte wie Zeit, Zustand, Gleichzeitigkeit, Reihenfolge, Kenntnis, Fehler und Übereinstimmung. Die Betonung liegt auf der Entwicklung allgemeiner Mechanismen, die auf eine Vielzahl von Problemen angewendet werden können. Sorgfältig ausgewählte Beispiele (Taktgeber, Sperren, Kameras, Sensoren, Controller, Slicer und Syncronizer) dienen gleichzeitig der Vertiefung theoretischer Aspekte und deren Umsetzung in die Praxis. Alle vorgestellten Algorithmen werden mit durchschaubaren, induktionsbasierten Verfahren bewiesen.
Author: Sushil K Prasad
Publisher: Morgan Kaufmann
ISBN: 0128039388
Category : Computers
Languages : en
Pages : 360
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Book Description
Topics in Parallel and Distributed Computing provides resources and guidance for those learning PDC as well as those teaching students new to the discipline. The pervasiveness of computing devices containing multicore CPUs and GPUs, including home and office PCs, laptops, and mobile devices, is making even common users dependent on parallel processing. Certainly, it is no longer sufficient for even basic programmers to acquire only the traditional sequential programming skills. The preceding trends point to the need for imparting a broad-based skill set in PDC technology. However, the rapid changes in computing hardware platforms and devices, languages, supporting programming environments, and research advances, poses a challenge both for newcomers and seasoned computer scientists. This edited collection has been developed over the past several years in conjunction with the IEEE technical committee on parallel processing (TCPP), which held several workshops and discussions on learning parallel computing and integrating parallel concepts into courses throughout computer science curricula. Contributed and developed by the leading minds in parallel computing research and instruction Provides resources and guidance for those learning PDC as well as those teaching students new to the discipline Succinctly addresses a range of parallel and distributed computing topics Pedagogically designed to ensure understanding by experienced engineers and newcomers Developed over the past several years in conjunction with the IEEE technical committee on parallel processing (TCPP), which held several workshops and discussions on learning parallel computing and integrating parallel concepts
Author: Giancarlo Zaccone
Publisher: Packt Publishing Ltd
ISBN: 1785286722
Category : Computers
Languages : en
Pages : 286
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Book Description
Master efficient parallel programming to build powerful applications using Python About This Book Design and implement efficient parallel software Master new programming techniques to address and solve complex programming problems Explore the world of parallel programming with this book, which is a go-to resource for different kinds of parallel computing tasks in Python, using examples and topics covered in great depth Who This Book Is For Python Parallel Programming Cookbook is intended for software developers who are well versed with Python and want to use parallel programming techniques to write powerful and efficient code. This book will help you master the basics and the advanced of parallel computing. What You Will Learn Synchronize multiple threads and processes to manage parallel tasks Implement message passing communication between processes to build parallel applications Program your own GPU cards to address complex problems Manage computing entities to execute distributed computational tasks Write efficient programs by adopting the event-driven programming model Explore the cloud technology with DJango and Google App Engine Apply parallel programming techniques that can lead to performance improvements In Detail Parallel programming techniques are required for a developer to get the best use of all the computational resources available today and to build efficient software systems. From multi-core to GPU systems up to the distributed architectures, the high computation of programs throughout requires the use of programming tools and software libraries. Because of this, it is becoming increasingly important to know what the parallel programming techniques are. Python is commonly used as even non-experts can easily deal with its concepts. This book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool. Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker. You will also understand the StarCluster framework, Pycsp, Scoop, and Disco modules in Python. Further on, you will learn GPU programming with Python using the PyCUDA module along with evaluating performance limitations. Next you will get acquainted with the cloud computing concepts in Python, using Google App Engine (GAE), and building your first application with GAE. Lastly, you will learn about grid computing concepts in Python and using PyGlobus toolkit, GFTP and GASS COPY to transfer files, and service monitoring in PyGlobus. Style and approach A step-by-step guide to parallel programming using Python, with recipes accompanied by one or more programming examples. It is a practically oriented book and has all the necessary underlying parallel computing concepts.
Author: David Reilly
Publisher: Addison-Wesley Professional
ISBN: 9780201710373
Category : Computers
Languages : en
Pages : 500
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Book Description
Java's rich, comprehensive networking interfaces make it an ideal platform for building today's networked, Internet-centered applications, components, and Web services. Now, two Java networking experts demystify Java's complex networking API, giving developers practical insight into the key techniques of network development, and providing extensive code examples that show exactly how it's done. David and Michael Reilly begin by reviewing fundamental Internet architecture and TCP/IP protocol concepts all network programmers need to understand, as well as general Java features and techniques that are especially important in network programming, such as exception handling and input/output. Using practical examples, they show how to write clients and servers using UDP and TCP; how to build multithreaded network applications; and how to utilize HTTP and access the Web using Java. The book includes detailed coverage of server-side application development; distributed computing development with RMI and CORBA; and email-enabling applications with the powerful JavaMail API. For all beginning to intermediate Java programmers, network programmers who need to learn to work with Java.
Author: A. Udaya Shankar
Publisher: Springer Science & Business Media
ISBN: 1461448816
Category : Computers
Languages : en
Pages : 386
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Book Description
Distributed Programming: Theory and Practice presents a practical and rigorous method to develop distributed programs that correctly implement their specifications. The method also covers how to write specifications and how to use them. Numerous examples such as bounded buffers, distributed locks, message-passing services, and distributed termination detection illustrate the method. Larger examples include data transfer protocols, distributed shared memory, and TCP network sockets. Distributed Programming: Theory and Practice bridges the gap between books that focus on specific concurrent programming languages and books that focus on distributed algorithms. Programs are written in a "real-life" programming notation, along the lines of Java and Python with explicit instantiation of threads and programs. Students and programmers will see these as programs and not "merely" algorithms in pseudo-code. The programs implement interesting algorithms and solve problems that are large enough to serve as projects in programming classes and software engineering classes. Exercises and examples are included at the end of each chapter with on-line access to the solutions. Distributed Programming: Theory and Practice is designed as an advanced-level text book for students in computer science and electrical engineering. Programmers, software engineers and researchers working in this field will also find this book useful.
Author: Yuan Tang
Publisher: Simon and Schuster
ISBN: 1638354197
Category : Computers
Languages : en
Pages : 375
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Book Description
Practical patterns for scaling machine learning from your laptop to a distributed cluster. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. This book reveals best practice techniques and insider tips for tackling the challenges of scaling machine learning systems. In Distributed Machine Learning Patterns you will learn how to: Apply distributed systems patterns to build scalable and reliable machine learning projects Build ML pipelines with data ingestion, distributed training, model serving, and more Automate ML tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows Make trade-offs between different patterns and approaches Manage and monitor machine learning workloads at scale Inside Distributed Machine Learning Patterns you’ll learn to apply established distributed systems patterns to machine learning projects—plus explore cutting-edge new patterns created specifically for machine learning. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Hands-on projects and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines. About the technology Deploying a machine learning application on a modern distributed system puts the spotlight on reliability, performance, security, and other operational concerns. In this in-depth guide, Yuan Tang, project lead of Argo and Kubeflow, shares patterns, examples, and hard-won insights on taking an ML model from a single device to a distributed cluster. About the book Distributed Machine Learning Patterns provides dozens of techniques for designing and deploying distributed machine learning systems. In it, you’ll learn patterns for distributed model training, managing unexpected failures, and dynamic model serving. You’ll appreciate the practical examples that accompany each pattern along with a full-scale project that implements distributed model training and inference with autoscaling on Kubernetes. What's inside Data ingestion, distributed training, model serving, and more Automating Kubernetes and TensorFlow with Kubeflow and Argo Workflows Manage and monitor workloads at scale About the reader For data analysts and engineers familiar with the basics of machine learning, Bash, Python, and Docker. About the author Yuan Tang is a project lead of Argo and Kubeflow, maintainer of TensorFlow and XGBoost, and author of numerous open source projects. Table of Contents PART 1 BASIC CONCEPTS AND BACKGROUND 1 Introduction to distributed machine learning systems PART 2 PATTERNS OF DISTRIBUTED MACHINE LEARNING SYSTEMS 2 Data ingestion patterns 3 Distributed training patterns 4 Model serving patterns 5 Workflow patterns 6 Operation patterns PART 3 BUILDING A DISTRIBUTED MACHINE LEARNING WORKFLOW 7 Project overview and system architecture 8 Overview of relevant technologies 9 A complete implementation
Author: David Peleg
Publisher: SIAM
ISBN: 0898714648
Category : Computers
Languages : en
Pages : 338
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Book Description
Gives a thorough exposition of network spanners and other locality-preserving network representations such as sparse covers and partitions.
