# Parallelize 'BiocParallel' functions

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The futurize package allows you to easily turn sequential code into parallel code by piping the sequential code to the `futurize()` function. Easy!

# TL;DR

You can use futurize to make BiocParallel functions parallelize via any of the [parallel backends] supported by Futureverse, e.g.

    library(futurize)
    plan(multisession)
    library(BiocParallel)

    slow_fcn <- function(x) {
      message("x = ", x)
      Sys.sleep(0.1)  # emulate work
      x^2
    }

    xs <- 1:10
    ys <- bplapply(xs, slow_fcn) |> futurize()

# Introduction

This vignette demonstrates how to use this approach to parallelize functions such as `bplapply()`, `bpmapply()`, and `bpvec()` in the BiocParallel package. For example, consider the `bplapply()` function. It works like base-R `lapply()`, but uses the BiocParallel framework to process the tasks concurrently. It is commonly used something like:

    library(BiocParallel)
    xs <- 1:1000
    ys <- bplapply(xs, slow_fcn)

The parallel backend is controlled by the `BiocParallel::register()`, similar to how we use `future::plan()` in Futureverse. We can use the futurize package to tell BiocParallel to hand over the orchestration of parallel tasks to Futureverse. All we need to do is to pass the expression to `futurize()` as in:

    library(BiocParallel)

    library(futurize)
    plan(multisession) ## parallelize on local machine

    xs <- 1:1000
    ys <- bplapply(xs, slow_fcn) |> futurize()
    #> x = 1
    #> x = 2
    #> x = 3
    #> ...
    #> x = 10

Note how messages produced on parallel workers are relayed as-is back to the main R session as they complete. Not only messages, but also warnings and other types of conditions are relayed back as-is. Likewise, standard output produced by `cat()`, `print()`, `str()`, and so on is relayed in the same way. This is a unique feature of Futureverse - other parallel frameworks in R, such as parallel, foreach with doParallel, and BiocParallel, silently drop standard output, messages, and warnings produced on workers. With futurize, your code behaves the same whether it runs sequentially or in parallel: nothing is lost in translation.

The built-in `multisession` backend parallelizes on your local computer and it works on all operating systems. There are other parallel backends to choose from, including alternatives to parallelize locally as well as distributed across remote machines, e.g.

    plan(future.mirai::mirai_multisession)

and

    plan(future.batchtools::batchtools_slurm)

# Supported Functions

The `futurize()` function supports parallelization of all BiocParallel functions that take argument `BPPARAM`. Specifically,

- `bplapply()` and `.bplapply_impl()`
- `bpmapply()` and `.bpmapply_impl()`
- `bpvec()`
- `bpaggregate()`

The following functions are currently not supported:

- `bpiterate()` - technically supported, but because this function does not support using `DoparParam()` with it, it effectively does not work with `futurize()`
- `bpvectorize()`
- `register()`

# Bioconductor packages using BiocParallel

Most Bioconductor packages that support parallelization do so via BiocParallel internally. These packages typically expose a `BPPARAM` argument in their functions, which controls the parallel backend used. For example, `DESeq2::DESeq()` has a `BPPARAM` argument that defaults to `BiocParallel::bpparam()`, which corresponds to the currently registered BiocParallel backend. This means that, in order to parallelize such a function, one can call `BiocParallel::register()` to set a parallel backend, and then the function will use it automatically.

However, not all packages default to `bpparam()`. For example, `sva::ComBat()` defaults to `bpparam("SerialParam")`, which means it always runs sequentially unless you explicitly pass a parallel `BPPARAM` argument. Because of this, one cannot count on `bpparam()` being the default everywhere - some functions require an explicit `BPPARAM` to parallelize. With futurize, this is handled automatically: `futurize()` injects the appropriate `BPPARAM` argument regardless of what the default is, so that the parallel execution is performed via the Futureverse, where the parallel backend is controlled by `future::plan()`.

# Progress Reporting via progressify

For progress reporting, please see the progressify package. It is specially designed to work with the Futureverse ecosystem and provide progress updates from parallelized computations in a near-live fashion. See the `vignette("futurize-11-apply", package = "futurize")` for more details and an example.