A Unified Approach to Concurrent, Parallel Map-Reduce in R using Futures

TL;DR

The paper introduces the futurize package for R, which simplifies parallelizing diverse map-reduce operations by providing a unified, easy-to-use interface that minimizes code refactoring and supports multiple frameworks.

Contribution

It presents a unified approach to parallel map-reduce in R through the futurize package, enabling seamless conversion of sequential code to parallel execution across various APIs.

Findings

Supports multiple map-reduce APIs in R

Enables minimal code changes for parallelization

Simplifies parallel computing with a unified interface

Abstract

The R ecosystem offers a rich variety of map-reduce application programming interfaces (APIs) for iterative computations, yet parallelizing code across these diverse frameworks requires learning multiple, often incompatible, parallel APIs. The futurize package addresses this challenge by providing a single function, futurize(), which transpiles sequential map-reduce expressions into their parallel equivalents in the future ecosystem, which performs all the heavy lifting. By leveraging R's native pipe operator, users can parallelize existing code with minimal refactoring -- often by simply appending `|> futurize()' to an expression. The package supports classical map-reduce functions from base R, purrr, crossmap, foreach, plyr, BiocParallel, e.g., lapply(xs, fcn) |> futurize() and map(xs, fcn) |> futurize(), as well as a growing set of domain-specific packages, e.g., boot, caret, glmnet, lme4, mgcv, and tm. By abstracting away the underlying parallel machinery, and unifying handling of future options, the package enables developers to declare what to parallelize via futurize(), and end-users to choose how via plan(). This article describes the philosophy, design, and implementation of futurize, demonstrates its usage across various map-reduce paradigms, and discusses its role in simplifying parallel computing in R.