LEADER 04918nam 2200637 450 001 9910813335003321 005 20200520144314.0 010 $a1-78398-927-0 035 $a(CKB)2670000000591655 035 $a(EBL)1935722 035 $a(SSID)ssj0001436056 035 $a(PQKBManifestationID)11803572 035 $a(PQKBTitleCode)TC0001436056 035 $a(PQKBWorkID)11434902 035 $a(PQKB)11229070 035 $a(Au-PeEL)EBL1935722 035 $a(CaPaEBR)ebr11015171 035 $a(CaONFJC)MIL718495 035 $a(OCoLC)902836197 035 $a(MiAaPQ)EBC1935722 035 $a(PPN)228046734 035 $a(EXLCZ)992670000000591655 100 $a20150212h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aR high performance programming $eovercome performance difficulties in R with a range of exciting techniques and solutions /$fAloysius Lim, William Tjhi 210 1$aBirmingham, England :$cPackt Publishing,$d2015. 210 4$dİ2015 215 $a1 online resource (176 p.) 225 1 $aCommunity Experience Distilled 300 $aIncludes index. 311 $a1-78398-926-2 311 $a1-322-87213-9 327 $aCover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Understanding R's Performance - Why Are R Programs Sometimes Slow?; Three constraints on computing performance - CPU, RAM, and disk I/O; R is interpreted on the fly; R is single-threaded; R requires all data to be loaded into memory; Algorithm design affects time and space complexity; Summary; Chapter 2: Profiling - Measuring Code's Performance; Measuring the total execution time; Measuring execution time with system.time(); Repeating time measurements with rbenchmark 327 $aMeasuring distribution of execution time with microbenchmarkProfiling the execution time; Profiling a function with Rprof(); The profiling results; Profiling the memory utilization; Monitoring memory utilization, CPU utilization, and disk I/O using OS tools; Identifying and resolving bottlenecks; Summary; Chapter 3: Simple Tweaks to Make R Run Faster; Vectorization; Use of built-in functions; Preallocating memory; Use of simpler data structures; Use of hash tables for frequent lookups on large data; Seek fast alternative packages in CRAN; Summary 327 $aChapter 4: Using Compiled Code for Greater SpeedCompiling R code before execution; Compiling functions; Just-in-time (JIT) compilation of R code; Using compiled languages in R; Prerequisites; Including compiled code inline; Calling external compiled code; Considerations for using compiled code; The R APIs; R data types versus native data types; Creating R objects and garbage collection; Allocating memory for non-R objects; Summary; Chapter 5: Using GPUs to Run R Even Faster; General purpose computing on GPUs; R and GPUs; Installing gputools; Fast statistical modeling in R with gputools 327 $aSummaryChapter 6: Simple Tweaks to Use Less RAM; Reusing objects without taking up more memory; Removing intermediate data when it is no longer needed; Calculating values on the fly instead of storing them persistently; Swapping active and non-active data; Summary; Chapter 7: Processing Large Datasets with Limited RAM; Using memory-efficient data structures; Smaller data types; Sparse matrices; Symmetric matrices; Bit vectors; Using memory-mapped files and processing data in chunks; The bigmemory package; The ff package; Summary; Chapter 8: Multiplying Performance with Parallel Computing 327 $aData parallelism versus task parallelismImplementing data parallel algorithms; Implementing task parallel algorithms; Running the same task on workers in a cluster; Running different tasks on workers in a cluster; Executing tasks in parallel on a cluster of computers; Shared memory versus distributed memory parallelism; Optimizing parallel performance; Summary; Chapter 9: Offloading Data Processing to Database Systems; Extracting data into R versus processing data in a database; Preprocessing data in a relational database using SQL; Converting R expressions into SQL; Using dplyr 327 $aUsing PivotalR 330 $aThis book is for programmers and developers who want to improve the performance of their R programs by making them run faster with large data sets or who are trying to solve a pesky performance problem. 410 0$aCommunity experience distilled. 606 $aR (Computer program language) 615 0$aR (Computer program language) 676 $a519.502855133 700 $aLim$b Aloysius$01610963 702 $aTjhi$b William 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910813335003321 996 $aR high performance programming$93938939 997 $aUNINA