Distributed Computing With Python

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Distributed Computing with Python

Author : Francesco Pierfederici
Publisher : Packt Publishing Ltd
ISBN 13 : 1785887041
Total Pages : 171 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Distributed Computing with Python by : Francesco Pierfederici

Download or read book Distributed Computing with Python written by Francesco Pierfederici and published by Packt Publishing Ltd. This book was released on 2016-04-12 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Concurrent and Distributed Computing in Java

Author : Vijay K. Garg
Publisher : John Wiley & Sons
ISBN 13 : 0471721263
Total Pages : 331 pages
Book Rating : 4.4/5 (717 download)

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Book Synopsis Concurrent and Distributed Computing in Java by : Vijay K. Garg

Download or read book Concurrent and Distributed Computing in Java written by Vijay K. Garg and published by John Wiley & Sons. This book was released on 2005-01-28 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Parallel Python with Dask

Author : Tim Peters
Publisher : GitforGits
ISBN 13 : 8119177460
Total Pages : 172 pages
Book Rating : 4.1/5 (191 download)

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Book Synopsis Parallel Python with Dask by : Tim Peters

Download or read book Parallel Python with Dask written by Tim Peters and published by GitforGits. This book was released on 2023-10-19 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Topics in Parallel and Distributed Computing

Author : Sushil K Prasad
Publisher : Morgan Kaufmann
ISBN 13 : 0128039388
Total Pages : 360 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Topics in Parallel and Distributed Computing by : Sushil K Prasad

Download or read book Topics in Parallel and Distributed Computing written by Sushil K Prasad and published by Morgan Kaufmann. This book was released on 2015-09-16 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Python Parallel Programming Cookbook

Author : Giancarlo Zaccone
Publisher : Packt Publishing Ltd
ISBN 13 : 1785286722
Total Pages : 286 pages
Book Rating : 4.7/5 (852 download)

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Book Synopsis Python Parallel Programming Cookbook by : Giancarlo Zaccone

Download or read book Python Parallel Programming Cookbook written by Giancarlo Zaccone and published by Packt Publishing Ltd. This book was released on 2015-08-26 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Java Network Programming and Distributed Computing

Author : David Reilly
Publisher : Addison-Wesley Professional
ISBN 13 : 9780201710373
Total Pages : 500 pages
Book Rating : 4.7/5 (13 download)

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Book Synopsis Java Network Programming and Distributed Computing by : David Reilly

Download or read book Java Network Programming and Distributed Computing written by David Reilly and published by Addison-Wesley Professional. This book was released on 2002 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Distributed Computing

Author : Ajay D. Kshemkalyani
Publisher : Cambridge University Press
ISBN 13 : 9780521189842
Total Pages : 0 pages
Book Rating : 4.1/5 (898 download)

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Book Synopsis Distributed Computing by : Ajay D. Kshemkalyani

Download or read book Distributed Computing written by Ajay D. Kshemkalyani and published by Cambridge University Press. This book was released on 2011-03-03 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing distributed computing systems is a complex process requiring a solid understanding of the design problems and the theoretical and practical aspects of their solutions. This comprehensive textbook covers the fundamental principles and models underlying the theory, algorithms and systems aspects of distributed computing. Broad and detailed coverage of the theory is balanced with practical systems-related issues such as mutual exclusion, deadlock detection, authentication, and failure recovery. Algorithms are carefully selected, lucidly presented, and described without complex proofs. Simple explanations and illustrations are used to elucidate the algorithms. Important emerging topics such as peer-to-peer networks and network security are also considered. With vital algorithms, numerous illustrations, examples and homework problems, this textbook is suitable for advanced undergraduate and graduate students of electrical and computer engineering and computer science. Practitioners in data networking and sensor networks will also find this a valuable resource. Additional resources are available online at www.cambridge.org/9780521876346.

Distributed Programming

Author : A. Udaya Shankar
Publisher : Springer Science & Business Media
ISBN 13 : 1461448816
Total Pages : 386 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Distributed Programming by : A. Udaya Shankar

Download or read book Distributed Programming written by A. Udaya Shankar and published by Springer Science & Business Media. This book was released on 2012-09-15 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Elements of Distributed Computing

Author : Vijay K. Garg
Publisher : John Wiley & Sons
ISBN 13 : 9780471036005
Total Pages : 448 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Elements of Distributed Computing by : Vijay K. Garg

Download or read book Elements of Distributed Computing written by Vijay K. Garg and published by John Wiley & Sons. This book was released on 2002-05-23 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Distributed Machine Learning Patterns

Author : Yuan Tang
Publisher : Simon and Schuster
ISBN 13 : 1638354197
Total Pages : 375 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Distributed Machine Learning Patterns by : Yuan Tang

Download or read book Distributed Machine Learning Patterns written by Yuan Tang and published by Simon and Schuster. This book was released on 2024-01-30 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Julia High Performance

Author : Avik Sengupta
Publisher : Packt Publishing Ltd
ISBN 13 : 1785887823
Total Pages : 132 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Julia High Performance by : Avik Sengupta

Download or read book Julia High Performance written by Avik Sengupta and published by Packt Publishing Ltd. This book was released on 2016-04-26 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: Design and develop high performing programs with Julia About This Book Learn to code high reliability and high performance programs Stand out from the crowd by developing code that runs faster than your peers' codes This book is intended for developers who are interested in high performance technical programming. Who This Book Is For This book is for beginner and intermediate Julia programmers who are interested in high performance technical computing. You will have a basic familiarity with Julia syntax, and have written some small programs in the language. What You Will Learn Discover the secrets behind Julia's speed Get a sense of the possibilities and limitations of Julia's performance Analyze the performance of Julia programs Measure the time and memory taken by Julia programs Create fast machine code using Julia's type information Define and call functions without compromising Julia's performance Understand number types in Julia Use Julia arrays to write high performance code Get an overview of Julia's distributed computing capabilities In Detail Julia is a high performance, high-level dynamic language designed to address the requirements of high-level numerical and scientific computing. Julia brings solutions to the complexities faced by developers while developing elegant and high performing code. Julia High Performance will take you on a journey to understand the performance characteristics of your Julia programs, and enables you to utilize the promise of near C levels of performance in Julia. You will learn to analyze and measure the performance of Julia code, understand how to avoid bottlenecks, and design your program for the highest possible performance. In this book, you will also see how Julia uses type information to achieve its performance goals, and how to use multuple dispatch to help the compiler to emit high performance machine code. Numbers and their arrays are obviously the key structures in scientific computing – you will see how Julia's design makes them fast. The last chapter will give you a taste of Julia's distributed computing capabilities. Style and approach This is a hands-on manual that will give you good explanations about the important concepts related to Julia programming.

Designing Distributed Systems

Author : Brendan Burns
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491983612
Total Pages : 164 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Designing Distributed Systems by : Brendan Burns

Download or read book Designing Distributed Systems written by Brendan Burns and published by "O'Reilly Media, Inc.". This book was released on 2018-02-20 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Without established design patterns to guide them, developers have had to build distributed systems from scratch, and most of these systems are very unique indeed. Today, the increasing use of containers has paved the way for core distributed system patterns and reusable containerized components. This practical guide presents a collection of repeatable, generic patterns to help make the development of reliable distributed systems far more approachable and efficient. Author Brendan Burns—Director of Engineering at Microsoft Azure—demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system. Understand how patterns and reusable components enable the rapid development of reliable distributed systems Use the side-car, adapter, and ambassador patterns to split your application into a group of containers on a single machine Explore loosely coupled multi-node distributed patterns for replication, scaling, and communication between the components Learn distributed system patterns for large-scale batch data processing covering work-queues, event-based processing, and coordinated workflows

Distributed Algorithms

Author : Wan Fokkink
Publisher : MIT Press
ISBN 13 : 0262026775
Total Pages : 242 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Distributed Algorithms by : Wan Fokkink

Download or read book Distributed Algorithms written by Wan Fokkink and published by MIT Press. This book was released on 2013-12-06 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to distributed algorithms that emphasizes examples and exercises rather than mathematical argumentation.

Guide to Reliable Distributed Systems

Author : Amy Elser
Publisher : Springer Science & Business Media
ISBN 13 : 1447124154
Total Pages : 733 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Guide to Reliable Distributed Systems by : Amy Elser

Download or read book Guide to Reliable Distributed Systems written by Amy Elser and published by Springer Science & Business Media. This book was released on 2012-01-15 with total page 733 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the key concepts, principles and implementation options for creating high-assurance cloud computing solutions. The guide starts with a broad technical overview and basic introduction to cloud computing, looking at the overall architecture of the cloud, client systems, the modern Internet and cloud computing data centers. It then delves into the core challenges of showing how reliability and fault-tolerance can be abstracted, how the resulting questions can be solved, and how the solutions can be leveraged to create a wide range of practical cloud applications. The author’s style is practical, and the guide should be readily understandable without any special background. Concrete examples are often drawn from real-world settings to illustrate key insights. Appendices show how the most important reliability models can be formalized, describe the API of the Isis2 platform, and offer more than 80 problems at varying levels of difficulty.

Mastering Large Datasets with Python

Author : John Wolohan
Publisher : Simon and Schuster
ISBN 13 : 1638350361
Total Pages : 451 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Mastering Large Datasets with Python by : John Wolohan

Download or read book Mastering Large Datasets with Python written by John Wolohan and published by Simon and Schuster. This book was released on 2020-01-15 with total page 451 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Modern data science solutions need to be clean, easy to read, and scalable. In Mastering Large Datasets with Python, author J.T. Wolohan teaches you how to take a small project and scale it up using a functionally influenced approach to Python coding. You’ll explore methods and built-in Python tools that lend themselves to clarity and scalability, like the high-performing parallelism method, as well as distributed technologies that allow for high data throughput. The abundant hands-on exercises in this practical tutorial will lock in these essential skills for any large-scale data science project. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Programming techniques that work well on laptop-sized data can slow to a crawl—or fail altogether—when applied to massive files or distributed datasets. By mastering the powerful map and reduce paradigm, along with the Python-based tools that support it, you can write data-centric applications that scale efficiently without requiring codebase rewrites as your requirements change. About the book Mastering Large Datasets with Python teaches you to write code that can handle datasets of any size. You’ll start with laptop-sized datasets that teach you to parallelize data analysis by breaking large tasks into smaller ones that can run simultaneously. You’ll then scale those same programs to industrial-sized datasets on a cluster of cloud servers. With the map and reduce paradigm firmly in place, you’ll explore tools like Hadoop and PySpark to efficiently process massive distributed datasets, speed up decision-making with machine learning, and simplify your data storage with AWS S3. What's inside An introduction to the map and reduce paradigm Parallelization with the multiprocessing module and pathos framework Hadoop and Spark for distributed computing Running AWS jobs to process large datasets About the reader For Python programmers who need to work faster with more data. About the author J. T. Wolohan is a lead data scientist at Booz Allen Hamilton, and a PhD researcher at Indiana University, Bloomington. Table of Contents: PART 1 1 ¦ Introduction 2 ¦ Accelerating large dataset work: Map and parallel computing 3 ¦ Function pipelines for mapping complex transformations 4 ¦ Processing large datasets with lazy workflows 5 ¦ Accumulation operations with reduce 6 ¦ Speeding up map and reduce with advanced parallelization PART 2 7 ¦ Processing truly big datasets with Hadoop and Spark 8 ¦ Best practices for large data with Apache Streaming and mrjob 9 ¦ PageRank with map and reduce in PySpark 10 ¦ Faster decision-making with machine learning and PySpark PART 3 11 ¦ Large datasets in the cloud with Amazon Web Services and S3 12 ¦ MapReduce in the cloud with Amazon’s Elastic MapReduce

Parallel Programming with Python

Author : Jan Palach
Publisher : Packt Publishing Ltd
ISBN 13 : 178328840X
Total Pages : 124 pages
Book Rating : 4.7/5 (832 download)

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Book Synopsis Parallel Programming with Python by : Jan Palach

Download or read book Parallel Programming with Python written by Jan Palach and published by Packt Publishing Ltd. This book was released on 2014-06-25 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fast, easy-to-follow and clear tutorial to help you develop Parallel computing systems using Python. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts and will help you in implementing these techniques in the real world. If you are an experienced Python programmer and are willing to utilize the available computing resources by parallelizing applications in a simple way, then this book is for you. You are required to have a basic knowledge of Python development to get the most of this book.

Java Distributed Computing

Author : Jim Farley
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491903341
Total Pages : 386 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Java Distributed Computing by : Jim Farley

Download or read book Java Distributed Computing written by Jim Farley and published by "O'Reilly Media, Inc.". This book was released on 1998-01-01 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Distributed computing and Java go together naturally. As the first language designed from the bottom up with networking in mind, Java makes it very easy for computers to cooperate. Even the simplest applet running in a browser is a distributed application, if you think about it. The client running the browser downloads and executes code that is delivered by some other system. But even this simple applet wouldn't be possible without Java's guarantees of portability and security: the applet can run on any platform, and can't sabotage its host.Of course, when we think of distributed computing, we usually think of applications more complex than a client and server communicating with the same protocol. We usually think in terms of programs that make remote procedure calls, access remote databases, and collaborate with others to produce a single result. Java Distributed Computing discusses how to design and write such applications. It covers Java's RMI (Remote Method Invocation) facility and CORBA, but it doesn't stop there; it tells you how to design your own protocols to build message passing systems and discusses how to use Java's security facilities, how to write multithreaded servers, and more. It pays special attention to distributed data systems, collaboration, and applications that have high bandwidth requirements.In the future, distributed computing can only become more important.Java Distributed Computing provides a broad introduction to the problems you'll face and the solutions you'll find as you write distributed computing applications.Topics covered in Java Distributed Computing: Introduction to Distributed Computing Networking Basics Distributed Objects (Overview of CORBA and RMI) Threads Security Message Passing Systems Distributed Data Systems (Databases) Bandwidth Limited Applications Collaborative Systems