Mastering Parallel Programming With R

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Mastering Parallel Programming With R

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Author : Simon R. Chapple
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-05-31

Mastering Parallel Programming With R written by Simon R. Chapple and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-31 with Computers categories.

Master the robust features of R parallel programming to accelerate your data science computations About This Book Create R programs that exploit the computational capability of your cloud platforms and computers to the fullest Become an expert in writing the most efficient and highest performance parallel algorithms in R Get to grips with the concept of parallelism to accelerate your existing R programs Who This Book Is For This book is for R programmers who want to step beyond its inherent single-threaded and restricted memory limitations and learn how to implement highly accelerated and scalable algorithms that are a necessity for the performant processing of Big Data. No previous knowledge of parallelism is required. This book also provides for the more advanced technical programmer seeking to go beyond high level parallel frameworks. What You Will Learn Create and structure efficient load-balanced parallel computation in R, using R's built-in parallel package Deploy and utilize cloud-based parallel infrastructure from R, including launching a distributed computation on Hadoop running on Amazon Web Services (AWS) Get accustomed to parallel efficiency, and apply simple techniques to benchmark, measure speed and target improvement in your own code Develop complex parallel processing algorithms with the standard Message Passing Interface (MPI) using RMPI, pbdMPI, and SPRINT packages Build and extend a parallel R package (SPRINT) with your own MPI-based routines Implement accelerated numerical functions in R utilizing the vector processing capability of your Graphics Processing Unit (GPU) with OpenCL Understand parallel programming pitfalls, such as deadlock and numerical instability, and the approaches to handle and avoid them Build a task farm master-worker, spatial grid, and hybrid parallel R programs In Detail R is one of the most popular programming languages used in data science. Applying R to big data and complex analytic tasks requires the harnessing of scalable compute resources. Mastering Parallel Programming with R presents a comprehensive and practical treatise on how to build highly scalable and efficient algorithms in R. It will teach you a variety of parallelization techniques, from simple use of R's built-in parallel package versions of lapply(), to high-level AWS cloud-based Hadoop and Apache Spark frameworks. It will also teach you low level scalable parallel programming using RMPI and pbdMPI for message passing, applicable to clusters and supercomputers, and how to exploit thousand-fold simple processor GPUs through ROpenCL. By the end of the book, you will understand the factors that influence parallel efficiency, including assessing code performance and implementing load balancing; pitfalls to avoid, including deadlock and numerical instability issues; how to structure your code and data for the most appropriate type of parallelism for your problem domain; and how to extract the maximum performance from your R code running on a variety of computer systems. Style and approach This book leads you chapter by chapter from the easy to more complex forms of parallelism. The author's insights are presented through clear practical examples applied to a range of different problems, with comprehensive reference information for each of the R packages employed. The book can be read from start to finish, or by dipping in chapter by chapter, as each chapter describes a specific parallel approach and technology, so can be read as a standalone.

Parallel R

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Author : Q. Ethan McCallum
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2011-10-21

Parallel R written by Q. Ethan McCallum and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-21 with Computers categories.

It’s tough to argue with R as a high-quality, cross-platform, open source statistical software product—unless you’re in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together. You’ll learn the basics of Snow, Multicore, Parallel, Segue, RHIPE, and Hadoop Streaming, including how to find them, how to use them, when they work well, and when they don’t. With these packages, you can overcome R’s single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address R’s memory barrier. Snow: works well in a traditional cluster environment Multicore: popular for multiprocessor and multicore computers Parallel: part of the upcoming R 2.14.0 release R+Hadoop: provides low-level access to a popular form of cluster computing RHIPE: uses Hadoop’s power with R’s language and interactive shell Segue: lets you use Elastic MapReduce as a backend for lapply-style operations

Mastering Concurrency In Python

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Author : Quan Nguyen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-11-27

Mastering Concurrency In Python written by Quan Nguyen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-27 with Computers categories.

Immerse yourself in the world of Python concurrency and tackle the most complex concurrent programming problems Key FeaturesExplore the core syntaxes, language features and modern patterns of concurrency in PythonUnderstand how to use concurrency to keep data consistent and applications responsiveUtilize application scaffolding to design highly-scalable programs Book Description Python is one of the most popular programming languages, with numerous libraries and frameworks that facilitate high-performance computing. Concurrency and parallelism in Python are essential when it comes to multiprocessing and multithreading; they behave differently, but their common aim is to reduce the execution time. This book serves as a comprehensive introduction to various advanced concepts in concurrent engineering and programming. Mastering Concurrency in Python starts by introducing the concepts and principles in concurrency, right from Amdahl's Law to multithreading programming, followed by elucidating multiprocessing programming, web scraping, and asynchronous I/O, together with common problems that engineers and programmers face in concurrent programming. Next, the book covers a number of advanced concepts in Python concurrency and how they interact with the Python ecosystem, including the Global Interpreter Lock (GIL). Finally, you'll learn how to solve real-world concurrency problems through examples. By the end of the book, you will have gained extensive theoretical knowledge of concurrency and the ways in which concurrency is supported by the Python language What you will learnExplore the concepts of concurrency in programmingExplore the core syntax and features that enable concurrency in PythonUnderstand the correct way to implement concurrencyAbstract methods to keep the data consistent in your programAnalyze problems commonly faced in concurrent programmingUse application scaffolding to design highly-scalable programsWho this book is for This book is for developers who wish to build high-performance applications and learn about signle-core, multicore programming or distributed concurrency. Some experience with Python programming language is assumed.

R High Performance Programming

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Author : Aloysius Lim
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-01-29

R High Performance Programming written by Aloysius Lim and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-29 with Computers categories.

This book is for programmers and developers who want to improve the performance of their R programs by making them run faster with large data sets or who are trying to solve a pesky performance problem.

Mastering Predictive Analytics With R

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Author : Rui Miguel Forte
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-06-17

Mastering Predictive Analytics With R written by Rui Miguel Forte and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-17 with Computers categories.

R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. With its constantly growing community and plethora of packages, R offers the functionality to deal with a truly vast array of problems. This book is designed to be both a guide and a reference for moving beyond the basics of predictive modeling. The book begins with a dedicated chapter on the language of models and the predictive modeling process. Each subsequent chapter tackles a particular type of model, such as neural networks, and focuses on the three important questions of how the model works, how to use R to train it, and how to measure and assess its performance using real world data sets. By the end of this book, you will have explored and tested the most popular modeling techniques in use on real world data sets and mastered a diverse range of techniques in predictive analytics.

Mastering Machine Learning With R

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Author : Cory Lesmeister
language : en
Publisher: Packt Publishing Ltd
Release Date : 2015-10-28

Mastering Machine Learning With R written by Cory Lesmeister and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-10-28 with Computers categories.

Master machine learning techniques with R to deliver insights for complex projects About This Book Get to grips with the application of Machine Learning methods using an extensive set of R packages Understand the benefits and potential pitfalls of using machine learning methods Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML system Who This Book Is For If you want to learn how to use R's machine learning capabilities to solve complex business problems, then this book is for you. Some experience with R and a working knowledge of basic statistical or machine learning will prove helpful. What You Will Learn Gain deep insights to learn the applications of machine learning tools to the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Familiarize yourself with fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Realize why and how to apply unsupervised learning methods In Detail Machine learning is a field of Artificial Intelligence to build systems that learn from data. Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning to your data. The book starts with introduction to Cross-Industry Standard Process for Data Mining. It takes you through Multivariate Regression in detail. Moving on, you will also address Classification and Regression trees. You will learn a couple of “Unsupervised techniques”. Finally, the book will walk you through text analysis and time series. The book will deliver practical and real-world solutions to problems and variety of tasks such as complex recommendation systems. By the end of this book, you will gain expertise in performing R machine learning and will be able to build complex ML projects using R and its packages. Style and approach This is a book explains complicated concepts with easy to follow theory and real-world, practical applications. It demonstrates the power of R and machine learning extensively while highlighting the constraints.

Mastering Functional Programming

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Author : Anatolii Kmetiuk
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-31

Mastering Functional Programming written by Anatolii Kmetiuk and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-31 with Computers categories.

Learn how functional programming can help you in deploying web servers and working with databases in a declarative and pure way Key Features Learn functional programming from scratch Program applications with side effects in a pure way Gain expertise in working with array tools for functional programming Book Description In large projects, it can get difficult keeping track of all the interdependencies of the code base and how its state changes at runtime. Functional Programming helps us solve these problems. It is a paradigm specifically designed to deal with the complexity of software development. This book will show you how the right abstractions can reduce complexity and make your code easy to read and understand. Mastering Functional Programming begins by touching upon the basics such as what lambdas are and how to write declarative code with the help of functions. It then moves on to more advanced concepts such as pure functions and type classes, the problems they aim to solve, and how to use them in real-world scenarios. You will also explore some of the more advanced patterns in the world of functional programming, such as monad transformers and Tagless Final. In the concluding chapters, you will be introduced to the actor model, implement it in modern functional languages, and explore the subject of parallel programming. By the end of the book, you will have mastered the concepts entailing functional programming along with object-oriented programming (OOP) to build robust applications. What you will learn Write reliable and scalable software based on solid foundations Explore the cutting edge of computer science research Effectively solve complex architectural problems in a robust way Avoid unwanted outcomes such as errors or delays and focus on business logic Write parallel programs in a functional style using the actor model Use functional data structures and collections in your day-to-day work Who this book is for If you are from an imperative and OOP background, this book will guide you through the world of functional programming, irrespective of which programming language you use.

Hands On Programming With R

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Author : Garrett Grolemund
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2014-06-13

Hands On Programming With R written by Garrett Grolemund and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-13 with Computers categories.

Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you’ll learn how to load data, assemble and disassemble data objects, navigate R’s environment system, write your own functions, and use all of R’s programming tools. RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You’ll gain valuable programming skills and support your work as a data scientist at the same time. Work hands-on with three practical data analysis projects based on casino games Store, retrieve, and change data values in your computer’s memory Write programs and simulations that outperform those written by typical R users Use R programming tools such as if else statements, for loops, and S3 classes Learn how to write lightning-fast vectorized R code Take advantage of R’s package system and debugging tools Practice and apply R programming concepts as you learn them

Mastering Cloud Computing

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Author : Rajkumar Buyya
language : en
Publisher: Newnes
Release Date : 2013-04-05

Mastering Cloud Computing written by Rajkumar Buyya and has been published by Newnes this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-04-05 with Computers categories.

Mastering Cloud Computing is designed for undergraduate students learning to develop cloud computing applications. Tomorrow's applications won’t live on a single computer but will be deployed from and reside on a virtual server, accessible anywhere, any time. Tomorrow's application developers need to understand the requirements of building apps for these virtual systems, including concurrent programming, high-performance computing, and data-intensive systems. The book introduces the principles of distributed and parallel computing underlying cloud architectures and specifically focuses on virtualization, thread programming, task programming, and map-reduce programming. There are examples demonstrating all of these and more, with exercises and labs throughout. Explains how to make design choices and tradeoffs to consider when building applications to run in a virtual cloud environment Real-world case studies include scientific, business, and energy-efficiency considerations

Parallel Computing For Data Science

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Author : Norman Matloff
language : en
Publisher: CRC Press
Release Date : 2015-06-04

Parallel Computing For Data Science written by Norman Matloff and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-04 with Computers categories.

Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic "n observations, p variables" matrix format but also from time series,