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Scalable Data Analysis in R Webinar Presentation

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Scalable Data Analysis in R Webinar Presentation

  1. 1. S c alable Data A nalys is in RL ee E . E dlefs enC hief S c ientis tWebinar Oc tober 26, 2011 1
  2. 2. Introduc tion Revolution Confidential Our ability to collect and store data has rapidlybeen outpacing our ability to analyze it We need scalable data analysis software R is the ideal platform for such software:universal data language, easy to add newfunctionality, powerful, flexible, forgiving I will discuss the approach to scalability we havetaken at Revolution Analytics with our packageRevoScaleR 2
  3. 3. R evoS c aleR pac kage Revolution Confidential Part of Revolution R Enterprise Provides data management and data analysis functionality Scales from small to huge data Is internally threaded to use multiple cores Distributes computations across multiple computers (in Version 5.0 beta on Windows) Revolution R Enterprise is free for academic use Revolution R Enterprise 3
  4. 4. Overview of my talk Revolution Confidential What do I mean by scalability? Why bother? Revolution’s approach to scalability:  R code stays the same as much as possible  “Chunking” – operate on chunks of data  Parallel external memory algorithms  Implementation issues Benchmarks Revolution R Enterprise 4
  5. 5. S c alability Revolution Confidential From small in-memory data.frames to multi- terabyte data sets distributed across space and even time From single cores on a single computer to multiple cores on multiple computers/nodes From local hardware to remote clouds For both data management and data analysis Revolution R Enterprise 5
  6. 6. K eys to s c alability Revolution Confidential Most importantly, must be able to process more data than can fit into memory on one computer at one time Process data in “chunks” Requires “external memory algorithms” Must be fast Must be capable of working on multiple platforms Revolution R Enterprise 6
  7. 7. Huge data s ets are bec oming c ommon Revolution Confidential Digital data is not only cheaper to store, it is cheaper to collect  More data is collected digitally  Increasingly more data of all types is being put directly into data bases It is easier now to merge and clean data, and there is a big and growing market for such data bases Revolution R Enterprise 7
  8. 8. Huge benefits to huge data Revolution Confidential More information, more to be learned Variables and relationships can be visualized and analyzed in much greater detail Can allow the data to speak for itself; can relax or eliminate assumptions Can get better predictions and better understandings of effects For more on this see the Revolution Analytics white paper at http://bit.ly/biganalytics Revolution R Enterprise 8
  9. 9. P oll Ques tion Revolution Confidential For you, how big is big data? Revolution R Enterprise 9
  10. 10. Two huge problems : c apac ity and s peed Revolution Confidential Capacity: problems handling the size of data sets or models  Data too big to fit into memory  Even if it can fit, there are limits on what can be done  Even simple data management can be extremely challenging Speed: even without a capacity limit, computation may be too slow to be useful 10
  11. 11. We are c urrently inc apable of analyzing alot of the data we have Revolution Confidential The most commonly-used statistical software tools either fail completely or are too slow to be useful on huge data sets In many ways we are back where we were in the ‘70s and ‘80’s in terms of ability to handle common data sizes Fortunately, this is changing Revolution R Enterprise 11
  12. 12. R equires s oftware s olutions Revolution Confidential For decades the rising tide of technology has allowed the same data analysis code to run faster and on bigger data sets That happy era has ended The size of data sets is increasing much more rapidly than the speed of single cores, of I/O, and of RAM Need software that can use multiple cores, multiple hard drives, and multiple computers Revolution R Enterprise 12
  13. 13. High P erformanc e A nalytic s : HPA Revolution Confidential HPA is HPC + Data High Performance Computing is CPU centric  Lots of processing on small amounts of data  Focus is on cores High Performance Analytics is data centric  Less processing per amount of data  Focus is on feeding data to the cores  On disk I/O  On efficient threading, data management in RAM Revolution R Enterprise 13
  14. 14. R evolution’s approac h to HPA ands c alability in the R evoS c aleR pac kage Revolution Confidential User interface  Set of R functions (but mostly implemented in C++)  Keep things as familiar as possible Capacity and speed  Handle data in large “chunks” – blocks of rows and columns  Use parallel external memory algorithms Revolution R Enterprise 14
  15. 15. S ome interfac e des ign goals Revolution Confidential Should be able to run the same analysis code on a small data.frame in memory and on a huge distributed data file on a remote cluster Should be able to do data transformations using standard R language on standard R objects Should be able to use the R formula language for analysis Revolution R Enterprise 15
  16. 16. S ample c ode for logit on laptop Revolution Confidential# # Standard formula language# # Allow transformations in the formula or# in a function or a list of expressions# # Row selections also allowed (not shown)rxLogit(ArrDelay>15 ~ Origin + Year + Month + DayOfWeek + UniqueCarrier + F(CRSDepTime), data=airData) Revolution R Enterprise 16
  17. 17. S ample c ode for logit on a c lus ter Revolution Confidential# Just change the “compute context”rxOptions(computeContext = myClust)# Otherwise, the code is the samerxLogit(ArrDelay>15 ~ Origin + Year + Month + DayOfWeek + UniqueCarrier + F(CRSDepTime), data=airData) Revolution R Enterprise 17
  18. 18. S ample data trans formation c ode Revolution Confidential# # Standard R code on standard R objects# # Function or a list of expressions# # In separate data step or in analysis call transforms <- list(DepTime = ConvertToDecimalTime(DepTime),Late = ArrDelay > 15) Revolution R Enterprise 18
  19. 19. C hunking Revolution Confidential Operate blocks of rows for selected columns Huge data can be processed a chunk at a time in a fixed amount of RAM Bigger is better up to a point  Makes it possible to take maximum advantage of disk bandwidth  Minimizes threading overhead  Can take maximum advantage of R’s vectorized code Revolution R Enterprise 19
  20. 20. T he bas is for a s olution for c apac ity, s peed,dis tributed and s treaming data – P E MA’sRevolution Confidential Parallel external memory algorithms (PEMA’s) allow solution of both capacity and speed problems, and can deal with distributed and streaming data External memory algorithms are those that allow computations to be split into pieces so that not all data has to be in memory at one time It is possible to “automatically” parallelize and distribute such algorithms 20
  21. 21. E xternal memory algorithms are widelyavailable Revolution Confidential Some statistics packages originating in the 70’s and 80’s, such as SAS and Gauss, were based almost entirely on external memory algorithms Examples of external memory algorithms:  data management (transformations, new variables, subsets, sorts, merges, converting to factors)  descriptive statistics, cross tabulations  linear models, glm, mixed effects  prediction/scoring  many maximum likelihood and other optimization algorithms  many clustering procedures, tree models 21
  22. 22. P arallel algorithms are widely available Revolution Confidential  Parallel algorithms are those that allow different portions of a computation to be done “at the same time” using different cores  There has been an lot of research on parallel computing over the past 20-30 years, and many helpful tools are now available 22
  23. 23. Not muc h literature on P E MA’s Revolution Confidential Unfortunately, there is not much literature on Parallel External Memory Algorithms, especially for statistical computations In what follows, I outline an approach to PEMA’s for doing statistical computations Revolution R Enterprise 23
  24. 24. P arallelizing external memory algorithmsRevolution Confidential Parallel algorithms split a job into tasks that can be run simultaneously External memory algorithms split a job into tasks that operate on separate blocks data External memory algorithms usually are run sequentially, but with proper care most can be parallelized efficiently and “automatically” The key is to split the tasks appropriately and to minimize inter-thread communication and synchronization 24
  25. 25. P arallel C omputations withS ync hronization and C ommunic ation Revolution Confidential 25
  26. 26. E mbarras s ingly P arallel C omputations Revolution Confidential 26
  27. 27. S equential E xternal Memory C omputations Revolution Confidential 27
  28. 28. P arallel E xternal Memory C omputations Revolution Confidential 28
  29. 29. E xample external memory algorithm for themean of a variable Revolution Confidential Initialization task: total=0, count=0 Process data task: for each block of x; total = sum(x), count=length(x) Update results task: combined total = sum(all totals), combined count = sum(all counts) Process results task: mean = combined total / combined count 29
  30. 30. P E MA’s in R evoS c aleR Revolution Confidential Analytics algorithms are implemented in a framework that automatically and efficiently parallelizes code Key is arranging code in virtual functions:  Initialize: allows initializations  ProcessData: process a chunk of data at a time  UpdateResults: updates one set of results from another  ProcessResults: any final processing Revolution R Enterprise 30
  31. 31. P E MA’s in R evoS c aleR (2) Revolution Confidential Analytic code is completely independent of:  Data path code that feeds data to ProcessData  Threading code for using multiple cores  Inter-computer communication code for distributing across nodes Communication among machines is highly abstracted (currently can use MPI, RPC) Implemented in C++ now, but we plan to add an implementation in R Revolution R Enterprise 31
  32. 32. S toring and reading data Revolution Confidential ScaleR can process data from a variety of sources Has its own optimized format (XDF) that is especially suitable for chunking Allows rapid access to blocks of rows for selected columns The time it takes to read a block of rows is essentially independent of the total number of rows and columns in the file Revolution R Enterprise 32
  33. 33. T he XDF file format Revolution Confidential Data is stored in blocks of rows per column “Header” information is stored at the end of the file Allows sequential reads; tens to hundreds of thousands of times faster than random reads Essentially unlimited in size  64 bit row indexing per column  64 bit column indexing (but the practical limit is much smaller) Revolution R Enterprise 33
  34. 34. T he XDF file format (2) Revolution Confidential Allows wide range of storage formats (1 byte to 8 byte signed and unsigned integers; 4 and 8 byte floats; variable length strings) Both new rows and new columns can be added to a file without having to rewrite the file Changes to header information (variable names, descriptions, factor levels, and so on) are extremely fast Revolution R Enterprise 34
  35. 35. Overview of data path on a c omputer Revolution Confidential DataSource object reads a chunk of data into a DataSet object on I/O thread DataSet is given to transformation code (data copied to R for R transformations); variables and rows may be created, removed Transformed DataSet is virtually split across computational cores and passed to ProcessData methods on different threads Any disk output is stored until I/O thread can write it to output DataSource Revolution R Enterprise 35
  36. 36. Handling data in memory Revolution Confidential Use of appropriate storage format reduces space and reduces time to move data in memory Data copying and conversion is minimized For instance, when adding a vector of unsigned shorts to a vector of doubles, the smaller type is not converted until loaded into the CPU Revolution R Enterprise 36
  37. 37. Us e of multiple c ores per c omputer Revolution Confidential Code is internally “threaded” so that inter- process communication and data transfer is not required One core (typically) handles I/O, while the other cores process data from the previous read Data is virtually split across computational cores; each core thinks it has its own private copy Revolution R Enterprise 37
  38. 38. R evoS c aleR – Multi-T hreaded P roc es s ing Revolution Confidential Shared Memory Data Data Data Core 0 Core 1 Core 2 Core n Disk (Thread 0) (Thread 1) (Thread 2) (Thread n) Multicore Processor (4, 8, 16+ cores) RevoScaleR • A RevoScaleR algorithm is provided a data source as input • The algorithm loops over data, reading a block at a time. Blocks of data are read by a separate worker thread (Thread 0). • Other worker threads (Threads 1..n) process the data block from the previous iteration of the data loop and update intermediate results objects in memory • When all of the data is processed a master results object is created from the intermediate results objects
  39. 39. Us e of multiple c omputers Revolution Confidential Key to efficiency is minimizing data transfer and communication Locality of data! For PEMA’s, the master node controls computations, telling workers where to get data and what computations to do Intermediate results on each node are aggregated across cores Master node gathers all results, checks for convergence, and repeats if necessary Revolution R Enterprise 39
  40. 40. R evoS c aleR – Dis tributed C omputing Revolution Confidential Compute Data Node Partition (RevoScaleR) • Portions of the data source are made available to each compute Compute node Data Node Partition • RevoScaleR on the master node (RevoScaleR) Master assigns a task to each compute node Node Compute (RevoScaleR) Data • Each compute node independently Node processes its data, and returns it’s Partition (RevoScaleR) intermediate results back to the master node Compute Data • master node aggregates all of the Node intermediate results from each Partition (RevoScaleR) compute node and produces the final result
  41. 41. Data management c apabilities Revolution Confidential Import from external sources Transform and clean variables Code and recode factors Missing values Validation Sort (huge data, but not distributed) Merge Aggregate, summarize Revolution R Enterprise 41
  42. 42. A nalys is algorithms Revolution Confidential Descriptive statistics (rxSummary) Tables and cubes (rxCube, rxCrossTabs) Correlations/covariances (rxCovCor, rxCor, rxCov, rxSSCP) Linear regressions (rxLinMod) Logistic regressions (rxLogit) K means clustering (rxKmeans) Predictions (scoring) (rxPredict) Other algorithms are being developed Revolution R Enterprise 42
  43. 43. C omparative benc hmark vs S A S Revolution Confidential In April 2011 SAS, “the undisputed leader in advanced analytics,” announced that it is developing advanced “high performance analytics” software Targeted to run on extremely expensive MPP database platforms from Greenplum and Teradata Key design feature is that all data must be in memory; thus huge data requires huge RAM Revolution R Enterprise 43
  44. 44. C omparis on with S A S HPA : logis ticregres s ion Revolution Confidential SAS and Greenplum ScaleR on commodity cluster Nodes 32 5 Cores 384 20 RAM 1,536 GB 80 GB Parameters “just a few” 7 Data location In memory On disk Rows of data 1 billion 1 billion Time 80 seconds 44 secondsRevo R is faster on the same amount of data, despite usingapproximately a 20th as many cores, a 20th as much RAM, a 6th asmany nodes, and not pre-loading data into RAM. Revolution R Enterprise 44
  45. 45. More C omparative benc hmarks Revolution Confidential SAS HPA benchmarks:  Logit, billion rows, 32 nodes, 384 cores, in-memory, “just a few” parameters: 80 secs.  Regression, 50 million rows, 24 nodes, in-memory, 1,800 parameters: 42 secs. ScaleR: 1 billion rows, 5 nodes, 20 cores ($5K)  rxLogit, 7 parameters: 44 secs  rxLinMod, 7 parameters: 4.1 secs  rxLinMod, 1,848 params: 11.9 secs  rxLogit, 1,848 params: 111.5 secs  rxLinMod, 13,537 params: 89.8 secs. Revolution R Enterprise 45
  46. 46. B enc hmarks us ing the airline data Revolution Confidential Airline on-time performance data produced by U.S. Department of Transportation; used in the ASA Data Expo 09 22 CSV files for the years 1987 – 2008 123.5 million observations, 29 variables, about 13 GB For benchmarks, sliced and replicated to get files with 1 million to about 1.25 billion rows 5 node commodity cluster (4 cores @ 3.2GHz & 16 GB RAM per node); Windows HPC Server 2008 Revolution R Enterprise 46
  47. 47. Dis tributed Import of A irline Data Revolution Confidential Copy the 22 CSV files to the 5 nodes, keeping rough balance in sizes Two pass import process:  Pass1: Import/clean/transform/append  Pass 2: Recode factors whose levels differ Import time: about 3 min 20 secs on 5 node cluster (about 17 minutes on single node) Revolution R Enterprise 47
  48. 48. S c alability of R evoS c aleR with R owsR egres s ion, 1 million - 1.1 billion rows , 443 betas Revolution Confidential (4 c ore laptop) Revolution R Enterprise 48
  49. 49. S c alability of R evoS c aleR with Nodes R egres s ion, 1 billion rows , 443 betas Revolution Confidential (1 to 5 nodes , 4 c ores per node) Revolution R Enterprise 49
  50. 50. S c alability of R evoS c aleR with Nodes Revolution ConfidentialL ogit, 123.5 million rows , 443 betas , 5 iterations (1 to 5 nodes , 4 c ores per node) Revolution R Enterprise 50
  51. 51. S c alability of R evoS c aleR with NodesL ogit, 1 billion rows , 443 betas , 5 iterations Confidential Revolution (1 to 5 nodes , 4 c ores per node) Revolution R Enterprise 51
  52. 52. P oll Ques tion Revolution Confidential Which big data problem do you most need to solve? Revolution R Enterprise 52
  53. 53. C ontac t information Revolution Confidential Lee Edlefsenlee@revolutionanalytics.com Revolution R Enterprise 53

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