# Parallelize R code on a Slurm cluster

Many computing-intensive processes in R involve the repeated evaluation of a function over many items or parameter sets. These so-called embarrassingly parallel calculations can be run serially with the `lapply` or `Map` function, or in parallel on a single machine with `mclapply` or `mcMap` (from the `parallel` package).

The rslurm package simplifies the process of distributing this type of calculation across a computing cluster that uses the Slurm workload manager. Its main function, `slurm_apply` (and the related `slurm_map`) automatically divide the computation over multiple nodes and write the necessary submission scripts. The package also includes functions to retrieve and combine the output from different nodes, as well as wrappers for common Slurm commands.

### Table of contents

- Basic example
- Single function evaluation
- Applying a function to a list of complex objects
- Adding auxiliary data and functions
- Configuring Slurm options
- Generating scripts for later submission
- How it works / advanced customization

## Basic example

To illustrate a typical rslurm workflow, we use a simple function that takes a mean and standard deviation as parameters, generates a million normal deviates and returns the sample mean and standard deviation.

    test_func <- function(par_mu, par_sd) {
        samp <- rnorm(10^6, par_mu, par_sd)
        c(s_mu = mean(samp), s_sd = sd(samp))
    }

We then create a parameter data frame where each row is a parameter set and each column matches an argument of the function.

    pars <- data.frame(par_mu = 1:10,
                       par_sd = seq(0.1, 1, length.out = 10))
    head(pars, 3)

    ##   par_mu par_sd
    ## 1      1    0.1
    ## 2      2    0.2
    ## 3      3    0.3

We can now pass that function and the parameters data frame to `slurm_apply`, specifiying the number of cluster nodes to use and the number of CPUs per node. The latter (`cpus_per_node`) determines how many processes will be forked on each node, as the `mc.cores` argument of `parallel::mcMap`.

    library(rslurm)
    sjob <- slurm_apply(test_func, pars, jobname = 'test_apply',
                        nodes = 2, cpus_per_node = 2, submit = FALSE)

    ## Submission scripts output in directory _rslurm_test_apply

The output of `slurm_apply` is a `slurm_job` object that stores a few pieces of information (job name, job ID, and the number of nodes) needed to retrieve the job's output.

The default argument `submit = TRUE` would submit a generated script to the Slurm cluster and print a message confirming the job has been submitted to Slurm, assuming your are running R on a Slurm head node. When working from a R session without direct access to the cluster, you must set `submit = FALSE`. Either way, the function creates a folder called `\_rslurm\_[jobname]` in the working directory that contains scripts and data files. This folder may be moved to a Slurm head node, the shell command `sbatch submit.sh` run from within the folder, and the folder moved back to your working directory. The contents of the `\_rslurm\_[jobname]` folder after completion of the `test_apply` job, i.e. following either manual or automatic (i.e. with `submit = TRUE`) submission to the cluster, includes one `results_*.RDS` file for each node:

    list.files('_rslurm_test_apply', 'results')

    ## [1] "results_0.RDS" "results_1.RDS"

The results from all the nodes can be read back into R with the `get_slurm_out()` function. In this example, `wait = FALSE`, but if you use the default argument `wait = TRUE`, execution will be paused until the Slurm job finishes running.

    res <- get_slurm_out(sjob, outtype = 'table', wait = FALSE)
    head(res, 3)

    ##       s_mu       s_sd
    ## 1 1.000137 0.09995552
    ## 2 2.000144 0.19988175
    ## 3 2.999822 0.30030102

The utility function `print_job_status` displays the status of a submitted job (i.e. in queue, running or completed), and `cancel_slurm` will remove a job from the queue, aborting its execution if necessary. These functions are R wrappers for the Slurm command line functions `squeue` and `scancel`, respectively.

When `outtype = 'table'`, the outputs from each function evaluation are row-bound into a single data frame; this is an appropriate format when the function returns a simple vector. The default `outtype = 'raw'` combines the outputs into a list and can thus handle arbitrarily complex return objects.

    res_raw <- get_slurm_out(sjob, outtype = 'raw', wait = FALSE)
    res_raw[1:3]

    ## [[1]]
    ##       s_mu       s_sd
    ## 1.00013690 0.09995552
    ##
    ## [[2]]
    ##      s_mu      s_sd
    ## 2.0001445 0.1998817
    ##
    ## [[3]]
    ##     s_mu     s_sd
    ## 2.999822 0.300301

The utility function `cleanup_files` deletes the temporary folder for the specified Slurm job.

    cleanup_files(sjob)

## Single function evaluation

In addition to `slurm_apply`, rslurm also defines a `slurm_call` function, which sends a single function call to the cluster. It is analogous in syntax to the base R function `do.call`, accepting a function and a named list of parameters as arguments.

    sjob <- slurm_call(test_func, jobname = 'test_call',
                       list(par_mu = 5, par_sd = 1), submit = FALSE)

    ## Submission scripts output in directory _rslurm_test_call

Because `slurm_call` involves a single process on a single node, it does not recognize the `nodes` and `cpus_per_node` arguments; otherwise, it accepts the same additional arguments (detailed in the sections below) as `slurm_apply`.

    cleanup_files(sjob)

## Applying a function to a list of complex objects

The function passed to `slurm_apply` can only receive atomic parameters stored within a data frame. Suppose we want instead to apply a function `func` to a list of complex R objects, `obj_list`. In that case we can use the function `slurm_map`, which is similar in syntax to `lapply` from base R and `map` from the `purrr` package. Its first argument is a list which can contain objects of any type, and its second argument is a function that acts on a single element of the list.

    sjob <- slurm_map(obj_list,
                      func,
                      nodes = 2, cpus_per_node = 2)

The output generated by `slurm_map` is structured the same way as `slurm_apply`. The procedures for checking the job status, extracting the results of the job, and cleaning up job files are also the same as described above.

## Adding auxiliary data and functions

Each of the tasks started by `slurm_apply` and `slurm_map` begin by default in an "empty" R environment containing only the function to be evaluated and its parameters. If we want to pass additional arguments to the function that do not vary with each task, we can simply add them as additional arguments to `slurm_apply` or `slurm_map`, like in this example, where we want to take the logarithm of many integers but always use `log(x, base = 2)`.

    sjob <- slurm_apply(log,
                        data.frame(x = 1:10000),
                        base = 2,
                        nodes = 2, cpus_per_node = 2)

To pass additional objects to the jobs that aren't explicitly included as arguments to the function passed to `slurm_apply` or `slurm_map`, we can use the argument `global_objects`. For example we might want to use an inline function that calls two other previously defined functions.

    sjob <- slurm_apply(function(a, b) c(func1(a),func2(b)),
                        data.frame(a, b),
                        global_objects = c("func1", "func2"),
                        nodes = 2, cpus_per_node = 2)

The `global_objects` argument specifies the names of any R objects (besides the parameters data frame) that must be accessed by the function passed to `slurm_apply`. These objects are saved to a `.RDS` file that is loaded on each cluster node prior to evaluating the function in parallel.

By default, all R packages attached to the current R session will also be attached (with `library`) on each cluster node, though this can be modified with the optional `pkgs` argument.

## Configuring Slurm options

Particular clusters may require the specification of additional Slurm options, such as time and memory limits for the job. The `slurm_options` argument allows you to set any of the command line options (view list) recognized by the Slurm `sbatch` command. It should be formatted as a named list, using the long names of each option (e.g. "time" rather than "t"). Flags, i.e. command line options that are toggled rather than set to a particular value, should be set to `TRUE` in `slurm_options`. For example, the following code sets the command line options `--time=1:00:00 --share`.

    sopt <- list(time = '1:00:00', share = TRUE)
    sjob <- slurm_apply(test_func, pars, slurm_options = sopt)

## How it works / advanced customization

As mentioned above, the `slurm_apply` function creates a job-specific folder. This folder contains the parameters as a RDS file and (if applicable) the objects specified as `global_objects` saved together in a RData file. The function also generates a R script (`slurm_run.R`) to be run on each cluster node, as well as a Bash script (`submit.sh`) to submit the job to Slurm.

More specifically, the Bash script tells Slurm to create a job array and the R script takes advantage of the unique `SLURM\_ARRAY\_TASK\_ID` environment variable that Slurm will set on each cluster node. This variable is read by `slurm_run.R`, which allows each instance of the script to operate on a different parameter subset and write its output to a different results file. The R script calls `parallel::mcMap` to parallelize calculations on each node.

Additionally, the `--dependency` option can be utilized by taking the job ID from the `slurm_job` object returned by `slurm_apply`, `slurm_map`, and `slurm_call` functions.
The ID can be manually added to the slurm options. In the following example, the job ID of `sjob1` is used to ensure that `sjob2` does not begin running until after `sjob1` finishes.

    # Job1
    sopt1 <- list(time = '1:00:00', share = TRUE)
    sjob1 <- slurm_apply(test_func, pars, slurm_options = sopt1)

    # Job2 depends on Job1
    pars2 <- data.frame(par_mu = 2:20,
                       par_sd = seq(0.2, 2, length.out = 20))
    sopt2 <- c(sopt1, list(dependency=sprintf("afterany:%s", sjob1$jobid)))
    sjob2 <- slurm_apply(test_func2, pars2, slurm_options = sopt2)

Both `slurm_run.R` and `submit.sh` are generated from templates, using the `whisker` package; these templates can be found in the `rslurm/templates` subfolder in your R package library. There are two templates for each script, one for `slurm_apply` and the other (with the word "single"" in its title) for `slurm_call`.

While you should avoid changing any existing lines in the template scripts, you may want to add `#SBATCH` lines to the `submit.sh` templates in order to permanently set certain Slurm command line options and thus customize the package to your particular cluster setup.