Parallelize 'pvclust' functions

Henrik Bengtsson

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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

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:

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():

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.

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.

plan(future.mirai::mirai_multisession)

and

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:

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().