Download or read book Parallel R written by Q. Ethan McCallum and published by "O'Reilly Media, Inc.". This book was released on 2011-10-21 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Parallel Computing for Data Science written by Norman Matloff and published by CRC Press. This book was released on 2015-06-04 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is one of the first parallel computing books to focus exclusively on parallel data structures, algorithms, software tools, and applications in data science. The book prepares readers to write effective parallel code in various languages and learn more about different R packages and other tools. It covers the classic n observations, p variables matrix format and common data structures. Many examples illustrate the range of issues encountered in parallel programming.
Download or read book Mastering Parallel Programming with R written by Simon R. Chapple and published by Packt Publishing Ltd. This book was released on 2016-05-31 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book R Programming for Data Science written by Roger D. Peng and published by . This book was released on 2012-04-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox.
Download or read book A Tour of Data Science written by Nailong Zhang and published by CRC Press. This book was released on 2020-11-11 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools – data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.
Download or read book Advanced R written by Hadley Wickham and published by CRC Press. This book was released on 2015-09-15 with total page 669 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what’s special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.
Download or read book Parallel Computing on Heterogeneous Networks written by Alexey L. Lastovetsky and published by John Wiley & Sons. This book was released on 2008-05-02 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: New approaches to parallel computing are being developed that make better use of the heterogeneous cluster architecture Provides a detailed introduction to parallel computing on heterogenous clusters All concepts and algorithms are illustrated with working programs that can be compiled and executed on any cluster The algorithms discussed have practical applications in a range of real-life parallel computing problems, such as the N-body problem, portfolio management, and the modeling of oil extraction
Download or read book Shared Memory Application Programming written by Victor Alessandrini and published by Morgan Kaufmann. This book was released on 2015-11-06 with total page 557 pages. Available in PDF, EPUB and Kindle. Book excerpt: Shared Memory Application Programming presents the key concepts and applications of parallel programming, in an accessible and engaging style applicable to developers across many domains. Multithreaded programming is today a core technology, at the basis of all software development projects in any branch of applied computer science. This book guides readers to develop insights about threaded programming and introduces two popular platforms for multicore development: OpenMP and Intel Threading Building Blocks (TBB). Author Victor Alessandrini leverages his rich experience to explain each platform’s design strategies, analyzing the focus and strengths underlying their often complementary capabilities, as well as their interoperability. The book is divided into two parts: the first develops the essential concepts of thread management and synchronization, discussing the way they are implemented in native multithreading libraries (Windows threads, Pthreads) as well as in the modern C++11 threads standard. The second provides an in-depth discussion of TBB and OpenMP including the latest features in OpenMP 4.0 extensions to ensure readers’ skills are fully up to date. Focus progressively shifts from traditional thread parallelism to modern task parallelism deployed by modern programming environments. Several chapter include examples drawn from a variety of disciplines, including molecular dynamics and image processing, with full source code and a software library incorporating a number of utilities that readers can adapt into their own projects. Designed to introduce threading and multicore programming to teach modern coding strategies for developers in applied computing Leverages author Victor Alessandrini's rich experience to explain each platform’s design strategies, analyzing the focus and strengths underlying their often complementary capabilities, as well as their interoperability Includes complete, up-to-date discussions of OpenMP 4.0 and TBB Based on the author’s training sessions, including information on source code and software libraries which can be repurposed
Download or read book Parallel R written by Ethan McCallum and published by "O'Reilly Media, Inc.". This book was released on 2011-10-28 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is a wonderful thing, indeed: in recent years this free, open-source product has become a popular toolkit for statistical analysis and programming. Two of R's limitations -- that it is single-threaded and memory-bound -- become especially troublesome in the current era of large-scale data analysis. It's possible to break past these boundaries by putting R on the parallel path. Parallel R will describe how to give R parallel muscle. Coverage will include stalwarts such as snow and multicore, and also newer techniques such as Hadoop and Amazon's cloud computing platform.
Download or read book Parallel Computing written by Barbara Chapman and published by IOS Press. This book was released on 2010 with total page 760 pages. Available in PDF, EPUB and Kindle. Book excerpt: From Multicores and GPUs to Petascale. Parallel computing technologies have brought dramatic changes to mainstream computing the majority of todays PCs, laptops and even notebooks incorporate multiprocessor chips with up to four processors. Standard components are increasingly combined with GPUs Graphics Processing Unit, originally designed for high-speed graphics processing, and FPGAs Free Programmable Gate Array to build parallel computers with a wide spectrum of high-speed processing functions. The scale of this powerful hardware is limited only by factors such as energy consumption and thermal control. However, in addition to"
Download or read book Cryptography in Constant Parallel Time written by Benny Applebaum and published by Springer Science & Business Media. This book was released on 2013-12-19 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Locally computable (NC0) functions are "simple" functions for which every bit of the output can be computed by reading a small number of bits of their input. The study of locally computable cryptography attempts to construct cryptographic functions that achieve this strong notion of simplicity and simultaneously provide a high level of security. Such constructions are highly parallelizable and they can be realized by Boolean circuits of constant depth. This book establishes, for the first time, the possibility of local implementations for many basic cryptographic primitives such as one-way functions, pseudorandom generators, encryption schemes and digital signatures. It also extends these results to other stronger notions of locality, and addresses a wide variety of fundamental questions about local cryptography. The author's related thesis was honorably mentioned (runner-up) for the ACM Dissertation Award in 2007, and this book includes some expanded sections and proofs, and notes on recent developments. The book assumes only a minimal background in computational complexity and cryptography and is therefore suitable for graduate students or researchers in related areas who are interested in parallel cryptography. It also introduces general techniques and tools which are likely to interest experts in the area.
Download or read book Mechanics for Beginners written by Isaac Todhunter and published by . This book was released on 1882 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The present work is constructed on the same plan as the author's 'Algebra for beginners' and 'Trigonometry for beginners'; and is intended as a companion to them. It is divided into short Chapters, and a collection of Examples follows each Chapter. Some of these examples are original, and others have been selected from College and University Examination papers. The work forms an elementary treatise on demonstrative mechanics"--Page v.
Download or read book Data Analysis with R Second Edition written by Anthony Fischetti and published by Packt Publishing Ltd. This book was released on 2018-03-28 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use. Key Features Analyze your data using R – the most powerful statistical programming language Learn how to implement applied statistics using practical use-cases Use popular R packages to work with unstructured and structured data Book Description Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. Starting with the basics of R and statistical reasoning, this book dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax with packages like Rcpp, ggplot2, and dplyr. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with messy data, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst. What you will learn Gain a thorough understanding of statistical reasoning and sampling theory Employ hypothesis testing to draw inferences from your data Learn Bayesian methods for estimating parameters Train regression, classification, and time series models Handle missing data gracefully using multiple imputation Identify and manage problematic data points Learn how to scale your analyses to larger data with Rcpp, data.table, dplyr, and parallelization Put best practices into effect to make your job easier and facilitate reproducibility Who this book is for Budding data scientists and data analysts who are new to the concept of data analysis, or who want to build efficient analytical models in R will find this book to be useful. No prior exposure to data analysis is needed, although a fundamental understanding of the R programming language is required to get the best out of this book.
Download or read book Parallel Kinematics written by Xin-Jun Liu and published by Springer Science & Business Media. This book was released on 2013-08-15 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parallel Kinematics- Type, Kinematics, and Optimal Design presents the results of 15 year's research on parallel mechanisms and parallel kinematics machines. This book covers the systematic classification of parallel mechanisms (PMs) as well as providing a large number of mechanical architectures of PMs available for use in practical applications. It focuses on the kinematic design of parallel robots. One successful application of parallel mechanisms in the field of machine tools, which is also called parallel kinematics machines, has been the emerging trend in advanced machine tools. The book describes not only the main aspects and important topics in parallel kinematics, but also references novel concepts and approaches, i.e. type synthesis based on evolution, performance evaluation and optimization based on screw theory, singularity model taking into account motion and force transmissibility, and others. This book is intended for researchers, scientists, engineers and postgraduates or above with interests in robotics and advanced machine tools technology such as parallel kinematics machines (PKMs). Xinjun Liu and Jinsong Wang, professors, work at The Institute of Manufacturing Engineering, Department of Precision Instruments and Mechanology, Tsinghua University.
Download or read book Electronics written by and published by . This book was released on 1945-07 with total page 1736 pages. Available in PDF, EPUB and Kindle. Book excerpt: June issues, 1941-44 and Nov. issue, 1945, include a buyers' guide section.
Download or read book Programming Environments for Massively Parallel Distributed Systems written by Karsten M. Decker and published by Springer Science & Business Media. This book was released on 1994 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Cray Research MPP Fortran Programming Model.- Resource Optimisation via Structured Parallel Programming.- SYNAPS/3 - An Extension of C for Scientific Computations.- The Pyramid Programming System.- Intelligent Algorithm Decomposition for Parallelism with Alfer.- Symbolic Array Data Flow Analysis and Pattern Recognition in Numerical Codes.- A GUI for Parallel Code Generation.- Formal Techniques Based on Nets, Object Orientation and Reusability for Rapid Prototyping of Complex Systems.- Adaptor - A Transformation Tool for HPF Programs.- A Parallel Framework for Unstructured Grid Solvers.- A Study of Software Development for High Performance Computing.- Parallel Computational Frames: An Approach to Parallel Application Development based on Message Passing Systems.- A Knowledge-Based Scientific Parallel Programming Environment.- Parallel Distributed Algorithm Design Through Specification Transformation: The Asynchronous Vision System.- Steps Towards Reusability and Portability in Parallel Programming.- An Environment for Portable Distributed Memory Parallel Programming.- Reuse, Portability and Parallel Libraries.- Assessing the Usability of Parallel Programming Systems: The Cowichan Problems.- Experimentally Assessing the Usability of Parallel Programming Systems.- Experiences with Parallel Programming Tools.- The MPI Message Passing Interface Standard.- An Efficient Implementation of MPI.- Post: A New Postal Delivery Model.- Asynchronous Backtrackable Communications in the SLOOP Object-Oriented Language.- A Parallel I/O System for High-Performance Distributed Computing.- Language and Compiler Support for Parallel I/O.- Locality in Scheduling Models of Parallel Computation.- A Load Balancing Algorithm for Massively Parallel Systems.- Static Performance Prediction in PCASE: A Programming Environment for Parallel Supercomputers.- A Performance Tool for High-Level Parallel Programming Languages.- Implementation of a Scalable Trace Analysis Tool.- The Design of a Tool for Parallel Program Performance Analysis and Tuning.- The MPP Apprentice Performance Tool: Delivering the Performance of the Cray T3D.- Optimized Record-Replay Mechanism for RPC-based Parallel Programming.- Abstract Debugging of Distributed Applications.- Design of a Parallel Object-Oriented Linear Algebra Library.- A Library for Coarse Grain Macro-Pipelining in Distributed Memory Architectures.- An Improved Massively Parallel Implementation of Colored Petri-Net Specifications.- A Tool for Parallel System Configuration and Program Mapping based on Genetic Algorithms.- Emulating a Paragon XP/S on a Network of Workstations.- Evaluating VLIW-in-the-large.- Implementing a N-Mixed Memory Model on a Distributed Memory System.- Working Group Report: Reducing the Complexity of Parallel Software Development.- Working Group Report: Usability of Parallel Programming System.- Working Group Report: Skeletons/Templates.
Download or read book R and Data Mining written by Yanchang Zhao and published by Academic Press. This book was released on 2012-12-31 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. Presents an introduction into using R for data mining applications, covering most popular data mining techniques Provides code examples and data so that readers can easily learn the techniques Features case studies in real-world applications to help readers apply the techniques in their work
