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 ```r library(futurize) plan(multisession) library(pvclust) data(mtcars, package = "datasets") fit <- pvclust(mtcars, nboot = 1000) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[pvclust]** functions, specifically `pvclust()`. The **[pvclust]** package provides hierarchical clustering with p-values (AU: Approximately Unbiased p-value, BP: Bootstrap Probability) calculated via multiscale bootstrap resampling. This method is computationally intensive because it requires repeating the clustering process for many bootstrap replicates at different scales. These calculations are naturally independent and thus excellent candidates for parallelization. ## Example: Hierarchical clustering with p-values The core function `pvclust()` performs multiscale bootstrap resampling to assess the uncertainty in hierarchical cluster analysis. For example, using the `mtcars` dataset: ```r library(pvclust) ## Assess the uncertainty of hierarchical clustering of mtcars ## variables using 1000 bootstrap replicates fit <- pvclust(mtcars, nboot = 1000) ``` Here `pvclust()` evaluates sequentially. We can easily make it evaluate in parallel by piping to `futurize()`: ```r library(futurize) library(pvclust) fit <- pvclust(mtcars, nboot = 1000) |> futurize() ``` This will distribute the bootstrap replications across the available parallel workers, given that we have set up parallel workers, e.g. ```r plan(multisession) ``` The built-in `multisession` backend parallelizes on your local computer and 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. ```r plan(future.mirai::mirai_multisession) ``` and ```r plan(future.batchtools::batchtools_slurm) ``` # Supported Functions The following **pvclust** function is supported by `futurize()`: * `pvclust()` with `seed = TRUE` as the default # Without futurize: Manual PSOCK cluster setup For comparison, here is what it takes to parallelize `pvclust()` using the **parallel** package directly, without **futurize**: ```r library(pvclust) library(parallel) ## Set up a PSOCK cluster ncpus <- 4L cl <- makeCluster(ncpus) ## Run pvclust in parallel fit <- pvclust(mtcars, nboot = 1000, parallel = cl) ## Tear down the cluster stopCluster(cl) ``` This requires you to manually create and manage the cluster lifecycle. If you forget to call `stopCluster()`, or if your code errors out before reaching it, you leak background R processes. You also have to decide upfront how many CPUs to use and what cluster type to use. Switching to another parallel backend, e.g. a Slurm cluster, would require a completely different setup. With **futurize**, all of this is handled for you - just pipe to `futurize()` and control the backend with `plan()`. [pvclust]: https://cran.r-project.org/package=pvclust [other parallel backends]: https://www.futureverse.org/backends.html