R (https://www.r-project.org) is a premier system for statistical and scientific computing and data science. At its core, R is a very carefully curated high-level interface to low-level numerical libraries. True to this principle, R packages have greatly expanded the scope and number of these interfaces over the years, among them interfaces to a large number of distributed and parallel computing tools. Despite its impressive breadth of sophisticated high-performance computing (HPC) tools, R is not often that widely used for “big” problems.
I believe the idiosyncrasies of most HPC technologies represent the major road block to their adoption (in any language or system). HPC technologies are often difficult to set up, use, and manage. They often rely on frequently changing and complex software library dependencies, and sometimes highly specific library versions. Managing all this boils down to spending more time on system administration, and less time on research.
How do we make things easier? One approach to help accelerate the adoption of HPC technology by the R community uses Singularity, a modern application containerization technique suited to HPC (http://singularity.lbl.gov/).
A container is a collection of the software requirements to run an application. Importantly, containers are defined and generated from a simple text recipe that can be easily communicated and versioned. Containers leverage modern operating system capabilities for virtualizing process and name spaces in a high-performance, low-overhead way. Container technology allows us to quickly turn recipes into runnable applications, and then deploy them anywhere.
The success of Docker, CoreOS, and related systems in enterprise business applications shows that there is a huge demand for lightweight, versionable, and portable containers. Notably, these technologies have not been all that widely successful in HPC settings, despite significant effort. Shifter (https://github.com/NERSC/shifter) is the most successful application of Docker to HPC, and while it is very impressive, it suffers from a few important drawbacks. The root-capable daemon program used by Docker is difficult to accommodate in many HPC environments. And the relatively heavy-weight nature of Docker virtualization can degrade the performance of high-performance hardware resources like Infiniband networking.
Singularity is a lightweight and very simple container technology that is particularly well-suited to HPC environments. Singularity virtualizes the minimum amount necessary to compute, allowing applications full access to fast hardware resources like Infiniband networks and GPUs. And Singularity runs without a server at all, eliminating possible server security exploits. The minimalist philosophy of Singularity makes it easy to install and run on everything from laptops to supercomputers, promoting the ability to quickly test containers before using them across large systems. Singularity is now widely available in supercomputer centers across the world.
Publishing results with code and data that can be reproduced and validated by others is an obviously important concept that has seen increased urgency these days. The idea is an old one that has been supported by S, S+ and R from the beginning with ideas like Sweave and more recently knitr and R markdown. R even promotes reproducible simulation in distributed/parallel settings by including high-quality, reproducible, distributed random number generators out of the box.
However, as R integrates with an increasing number of external libraries and frameworks like cuDNN, Spark, and others, the ability to reproduce the software environment that R runs in is becoming both more important and more complex. Containers help us define these complex set ups with simple, versionable text files, and then portably run them in diverse environments.
The following examples assume that Singularity is installed on your system. See http://singularity.lbl.gov/ for details – it’s very easy to install. The examples can be run from nearly any modern Unix operating system, although the processor architecture must be supported by the container operating system.
The first example below shows a canonical “hello world” program. Instead of a completely trivial example, we print “Hello, TensorFlow!” using TensorFlow from R via Python (https://github.com/tensorflow/tensorflow, https://github.com/python/cpython), introducing a complex but typical software dependency chain. A test program validates operation by printing the “hello world” message from R through Tensorflow. The container generically will run any R program named
main.R in its working directory.
Here is the Singularity container definition file for the example using the Ubuntu Xenial operating system. (Note that you can build a container from this definition file on any Singularity-supported operating system.)
BootStrap: debootstrap OSVersion: xenial MirrorURL: http://archive.ubuntu.com/ubuntu/ %post sed -i 's/main/main restricted universe/g' /etc/apt/sources.list apt-get update # Install R, Python, misc. utilities apt-get install -y libopenblas-dev r-base-core libcurl4-openssl-dev libopenmpi-dev openmpi-bin openmpi-common openmpi-doc openssh-client openssh-server libssh-dev wget vim git nano git cmake gfortran g++ curl wget python autoconf bzip2 libtool libtool-bin python-pip python-dev apt-get clean locale-gen en_US.UTF-8 # Install Tensorflow pip install tensorflow # Install required R packages R --slave -e 'install.packages("devtools", repos="https://cloud.r-project.org/")' R --slave -e 'devtools::install_github("rstudio/tensorflow")' %test #!/bin/sh exec R --slave -e "library(tensorflow); \ sess <- tensorflow::tf\$Session(); \ hello <- tensorflow::tf\$constant('Hello, TensorFlow!'); \ sess\$run(hello)" %runscript #!/bin/bash Rscript --slave "main.R"
TIP If you’re running on Red Hat or CentOS, you’ll need the debootstrap program:
sudo yum install debootstrap. See the Singularity documentation for more information.
Assuming that the above definition file is named
tensorflow.def, you can bootstrap a Singularity container image named
sudo rm -f tensorflow.img && \ sudo singularity create --size 4000 tensorflow.img && \ sudo singularity bootstrap tensorflow.img tensorflow.def
%post section of the definition file installs R, Python, Tensorflow and miscellaneous utilities into the container. The
%test section runs the “hello world” program as an example to verify things are working. The
%run section of this example simply runs an arbitrary user R program named
main.R in the container’s working directory.
Run the “hello world”
%test script with:
singularity test tensorflow.img
I love Singularity’s ability to include unit tests in container definition files – it reminds me of building R packages! I encourage using the test section judiciously to confirm that the container will work as intended.
You can run an arbitrary R program in the container by creating a
main.R file in the container working directory and running:
singularity run tensorflow.img
The previous example illustrated a complex tool chain, but only running on a single computer. This example is closer to a complete distributed R application.
Genomic variants record differences in a genome relative to a reference. Many types of differences exist, see for instance https://en.wikipedia.org/wiki/Structural_variation. This example focuses on differences among the 2,504 whole human genomes curated by the 1000 Genomes Project (see: “A global reference for human genetic variation”, The 1000 Genomes Project Consortium, Nature 526, 68-74 (01 October 2015) doi:10.1038/nature15393). The example downloads whole genome data files in VCF 4.1 format. Although the 1000 Genome Project data files are used here, the example will work for any input set of VCF files (it processes all files named
*.vcf.gz in the working directory).
The example constructs a sparse 2,504 row (people) by 81,271,844 column (genomic variants) R matrix from the VCF data files. The matrix entries are one if a particular variant occurs in the person, or a zero otherwise. Because not every person exhibits every variant, the matrix is very sparse with about 9.8 billion nonzero-elements, or about 2% fill-in. Rather than construct a single giant sparse matrix, the example partitions the data and saves many smaller sub-matrices each with CHUNKSIZE non-zero elements as R data files in the working directory, where CHUNKSIZE is an optional user-defined parameter that defaults to a value based on system memory size.
The example computes the first NCOMP principal components, where NCOMP is a user-specified environment variable specified by the user, of sparse genomic variant VCF files. The example is very general, requiring an arbitrary number of VCF data files as input and running on any number of computers. It uses MPI to coordinate parallel activity across computers, along with the
foreach packages in R. The choice of MPI is well-suited to supercomputer deployment, and the example assumes that MPI is available along with the following assumptions:
A successful run produces the following output:
This example was designed for deployment with supercomputer systems in mind. See https://github.com/bwlewis/1000_genomes_examples for other implementations that don’t require MPI.
Singularity encapsulates the program logic and the external library dependency chain (MPI, etc.) required by the computation in the following definition file:
BootStrap: debootstrap OSVersion: xenial MirrorURL: http://archive.ubuntu.com/ubuntu/ Include: bash %post sed -i 's/main/main restricted universe/g' /etc/apt/sources.list apt-get update # Install R, openmpi, misc. utilities: apt-get install -y libopenblas-dev r-base-core libcurl4-openssl-dev libopenmpi-dev openmpi-bin openmpi-common openmpi-doc openssh-client openssh-server libssh-dev wget vim git nano git cmake gfortran g++ curl wget python autoconf bzip2 libtool libtool-bin apt-get clean # Install required R packages R --slave -e 'install.packages(c("irlba", "doMPI"), repos="https://cloud.r-project.org/")' # Install simple VCF parser helper wget https://raw.githubusercontent.com/bwlewis/1000_genomes_examples/master/parse.c && cc -O2 parse.c && mv a.out /usr/local/bin/parsevcf && rm parse.c # Set up unit test mkdir -p /usr/local/share/R chmod a+rwx /usr/local/share/R wget https://raw.githubusercontent.com/bwlewis/1000_genomes_examples/master/unit.R && mv unit.R /usr/local/share/R/ # This is the main R program run by /singularity wget https://raw.githubusercontent.com/bwlewis/1000_genomes_examples/master/pca-mpi.R && mv pca-mpi.R /usr/local/share/R/ %test #!/bin/sh exec Rscript --slave "/usr/local/share/R/unit.R" %runscript #!/bin/bash Rscript --slave "/usr/local/share/R/pca-mpi.R"
Build and bootstrap a Singularity container using the
variant_pca.def definition file with:
sudo rm -f variant_pca.img && \ sudo singularity create --size 4000 variant_pca.img && \ sudo singularity bootstrap variant_pca.img variant_pca.def
The container includes a simple unit test that verifies MPI operation invoked by:
mpirun -np 4 singularity test variant_pca.img
A small, fast-running example computes principal components for the first 10,000 variants from the 1000 Genomes Project chromosomes 21 and 22 as follows:
wget https://raw.githubusercontent.com/bwlewis/1000_genomes_examples/extra/chr21.head.vcf.gz wget https://raw.githubusercontent.com/bwlewis/1000_genomes_examples/extra/chr22.head.vcf.gz LANG=C CHUNKSIZE=10000000 mpirun -x LANG -x CHUNKSIZE -np 2 singularity run -H $(pwd) variant_pca.img
Read the output pca.rdata file from R using
readRDS(). The following code plots the first three estimated principal components.
x <- readRDS('pca.rdata') library(lattice) splom(x$v)