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019    952413857 
020    9781784394622|q(electronic book) 
020    1784394629|q(electronic book) 
020    |z1784394009 
020    |z9781784394004 
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037    CL0500000751|bSafari Books Online 
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245 00 Mastering parallel programming with R :|bmaster the robust
       features of R parallel programming to accelerate your data
       science computations /|cSimon R. Chapple, Eilidh Troup, 
       Thorsten Forster, Terence Sloan. 
264  1 Birmingham, UK :|bPackt Publishing Limited,|c2016. 
300    1 online resource. 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    text file|2rdaft 
490 1  Community experience distilled 
500    Includes index. 
520 8  Annotation|bMaster the robust features of R parallel 
       programming to accelerate your data science 
       computationsAbout 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 programsWho This Book Is 
       ForThis 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 programsIn DetailR 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. 
588 0  Online resource, title from PDF title page (Ebsco, viewed 
       on July 28, 2016). 
590    eBooks on EBSCOhost|bEBSCO eBook Subscription Academic 
       Collection - North America 
650  0 Parallel programming (Computer science)|0https://
       id.loc.gov/authorities/subjects/sh85097827 
650  0 R (Computer program language)|0https://id.loc.gov/
       authorities/subjects/sh2002004407 
650  7 Parallel programming (Computer science)|2fast|0https://
       id.worldcat.org/fast/1052939 
650  7 R (Computer program language)|2fast|0https://
       id.worldcat.org/fast/1086207 
655  4 Electronic books. 
700 1  Chapple, Simon R.,|eauthor. 
700 1  Troup, Eilidh,|eauthor. 
700 1  Forster, Thorsten,|eauthor. 
700 1  Sloan, Terence,|eauthor. 
776 08 |iPrint version:|z1784394009|z9781784394004
       |w(OCoLC)949750993 
830  0 Community experience distilled.|0https://id.loc.gov/
       authorities/names/no2011030603 
856 40 |uhttps://rider.idm.oclc.org/login?url=http://
       search.ebscohost.com/login.aspx?direct=true&scope=site&
       db=nlebk&AN=1243721|zOnline eBook via EBSCO. Access 
       restricted to current Rider University students, faculty, 
       and staff. 
856 42 |3Instructions for reading/downloading the EBSCO version 
       of this eBook|uhttp://guides.rider.edu/ebooks/ebsco 
901    MARCIVE 20231220 
948    |d20190118|cEBSCO|tEBSCOebooksacademic NEW 1-11-19 6702 
       |lridw 
994    92|bRID