R on BioHPC. Rstudio, Parallel R and BioconductoR. Updated for

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1 R on BioHPC Rstudio, Parallel R and BioconductoR 1 Updated for

2 2 Today we ll be looking at

3 Why R? The dominant statistics environment in academia Large number of packages to do a lot of different analyses Excellent uptake in Bioinformatics specialist packages (Relatively) easy to accomplish complex stats work Very active development right now R Foundation, R Consortium, Revolution Analytics, RStudio, Microsoft 3

4 Why not R? Quirky language painful for e.g. Python programmers Generally thought to be quite slow except for optimized linear algebra Complex old-fashioned documentation Parallelization packages can be complex / outdated but it s getting better quickly. 4

5 5 Exciting Recent Developments in R

6 RStudio An IDE for R, on the web BioHPC optimized R, access to cluster storage, persistent sessions 6

7 When to use RStudio Development work with small datasets Creating R Markdown documents Working with Shiny for dataset visualizations Any small, short-running data analysis tasks Large datasets, very long running jobs, parallel code? Must use R on the cluster 7

8 Using R on the cluster / clients module load R/3.2.1-intel Latest version, optimized, same as used by rstudio.biohpc.swmed.edu 8

9 Installing Packages We have a set of common packages pre-installed in the R module. You can install your own into your home directory (~/R) install.packages(c("microbenchmark", "data.table")) Some packages need additional libraries, won t compile successfully. - Ask us to install them for you (biohpc-help@utsouthwestern.edu) This is for packages from CRAN BioconductoR packages install differently See later! 9

10 Our R is faster than standard downloads mkl_test.r Compiled using Intel compiler and Intel Math Kernel Library Task Standard R BioHPC R Speedup Matrix Multiplication x Cholesky Decomposition x SVD x PCA x LDA x This is on a cluster node speedup is less on clients with fewer CPU cores For your own Mac or PC see 10

11 Benchmarking functions in R (and compiling them) functions.r library(compiler) f <- function(n, x) for (i in 1:n) x = (1 + sin(x))^(cos(x)) g <- cmpfun(f) library(microbenchmark) compare <- microbenchmark(f(1000, 1), g(1000, 1), times = 1000) library(ggplot2) autoplot(compare) Compiling a function that is called often can increase speed The microbenchmark package allows you to benchmark functions 11

12 For speed always vectorize! functions.r distnorm <- function(){ } x <- seq(-5, 5, 0.01) y <- rep(na,length(x)) for(i in 1:length(x)) { y[i] <- stdnorm(x[i]) } return(list(x=x,y=y)) 54x speedup! vdistnorm <- function(){ } x <- seq(-5, 5, 0.01) y <- stdnorm(x) return(list(x=x, y=y)) Using a function compilation improved median some (< 2x) Using vector form was much faster 12

13 Explicit Parallelization in R Our optimized R automatically parallelizes linear algebra on a single machine - enough in a lot of cases! Always prefer using vector/matrix form over for loops and apply functions to get the most out of these optimizations. If you need more options you can control the parallelization: library(parallel) library(doparallel) # Single-node and cluster parallelization # apply functions and explicit execution # Simple parallel foreach loops Can run parallel code on a single node (multicore) or across nodes (MPI) 13

14 Our Example Application mc_parallel.r # Define a function that performs a random walk with a # specified bias that decays rw2d <- function(n, mu, sigma){ steps=matrix(, nrow=n, ncol=2) for (i in 1:n){ steps[i,1] <- rnorm(1, mean=mu, sd=sigma ) steps[i,2] <- rnorm(1, mean=mu, sd=sigma ) mu <- mu/2 } return( apply(steps, 2, cumsum) ) } 14

15 A bigger task mc_parallel.r # Generate random walks of lengths between 1000 and 5000 # foreach loop system.time( results <- foreach(l=1000:5000) %do% rw2d(l, 3, 1) ) # user system elapsed # # Apply system.time( results <- lapply( 1000:5000, rw2d, 3, 1) ) # user system elapsed #

16 Start a cluster (of R slave workers on a single machine) mc_parallel.r Single node, multiple cores running multiple R slaves #Parallel Single node library(parallel) library(doparallel) # Create a cluster of workers using all cores cl <- makecluster( detectcores() ) # Tell foreach with %dopar% to use this cluster registerdoparallel(cl) stopcluster(cl) 16

17 R parallel vs MKL conflict Intel MKL tries to use all cores for every linear algebra operation R is running multiple iterations of a loop in parallel using all cores If used together too many threads/processes are launched far more than cores! export OMP_NUM_THREADS=1 sys.setenv(omp_num_threads="1") # on terminal before running R # within R ~ 5% improvement by disabling MKL multi-threading 17

18 This time in parallel! mc_parallel.sh cl <- makecluster( detectcores() ) RegisterDoParallel(cl) Sys.setenv(OMP_NUM_THREADS="1") # Generate 1000 random walks of increasing length # Parallel foreach loop system.time( results <- foreach(l=1000:5000) %dopar% rw2d(l, 3, 1) ) # user system elapsed # # Parallel apply system.time( results <- parlapply( cl, 1000:5000, rw2d, 3, 1) ) # user system elapsed # stopcluster(cl) 5x Speedup 9x Speedup 18

19 MPI parallelization for really big jobs MPI is available on R/3.1.2-intel only We will continue to use the simple parallel and doparallel packages Lots online about snow this is now behind the scenes in new versions of R Please join us for coffee to discuss MPI projects using R Work in progress optimizations with your help 19

20 MPI parallelization easy! mpi_parallel.r Just one change in R code! cl <- makecluster( 128, type="mpi" ) Number of MPI tasks cores per node * nodes (or less if RAM limited) 48 cores per node for 256GB partition 32 cores per node for other partitions 20

21 MPI parallelization submitting the job mpi_parallel.sh #!/bin/bash #SBATCH --job-name R_MPI_TEST # Number of nodes required to run this job #SBATCH -N 4 # Distribute n tasks per node #SBATCH --ntasks-per-node=32 #SBATCH -t 0-2:0:0 #SBATCH -o job_%j.out #SBATCH -e job_%j.err #SBATCH --mail-type ALL #SBATCH --mail-user david.trudgian@utsouthwestern.edu module load R/3.2.1-intel ulimit -l unlimited R --vanilla < mpi_parallel.r No mpirun! # END OF SCRIPT 21

22 MPI Performance # Sequential (with MKL multi-threading) system.time( results <- lapply( 1000:10000, rw2d, 3, 1) ) # user system elapsed # # Parallel apply, 4 nodes, 128 MPI tasks system.time( results <- parlapply( cl, 1000:10000, rw2d, 3, 1) ) # user system elapsed # x Speedup 22

23 BioconductoR A comprehensive set of Bioinformatics related packages for R Software and datasets 23

24 BioconductoR Base packages installed, plus some commonly used extras Install additional packages to home directory: source(" bioclite('limma') Ask for packages that fail to compile 24

25 BioconductoR Bioconductor workflows are fantastic tutorials 25

26 BioconductoR Example DEMO RNA-Seq Analysis & UCSC Genome Browser See bioconductor.rmd 26

27 Dallas R Users Group University of Dallas, Irving, Saturdays (Accessible by DART Orange Line) 27