Parallelize 'mgcv' 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(mgcv)

## Adopted from example("bam", package = "mgcv")
dat <- gamSim(1, n = 25000, dist = "normal", scale = 20)
bs <- "cr"
k <- 12

b <- bam(y ~ s(x0, bs = bs) + s(x1, bs = bs) + s(x2, bs = bs, k = k) +
             s(x3, bs = bs), data = dat) |> futurize()

Introduction

This vignette demonstrates how to use this approach to parallelize mgcv functions such as bam().

The mgcv package is one of the “recommended” packages in R. It provides methods for fitting Generalized Additive Models (GAMs). The bam() function can be used to fit GAMs for massive datasets (“Big Additive Models”) with many thousands of observations, making it an excellent candidate for parallelization.

Example: Fitting a Big Additive Model

The bam() function supports parallel processing by setting up a parallel cluster and passing it as argument cluster. This is abstracted away by futurize:

library(mgcv)

## Adopted from example("bam", package = "mgcv")
dat <- gamSim(1, n = 25000, dist = "normal", scale = 20)
bs <- "cr"
k <- 12

b <- bam(y ~ s(x0, bs = bs) + s(x1, bs = bs) + s(x2, bs = bs, k = k) +
             s(x3, bs = bs), data = dat) |> futurize()

This will distribute the calculations 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 mgcv functions are supported by futurize():

• bam()
• predict.bam()