OpenMP Support Comes to {macrtools}

New openmp_*() functions bring automatic parallel computing setup to macOS R users

The {macrtools} package now includes support for automatically setting up OpenMP for R on macOS through five new openmp_*() functions. These functions address the longstanding issue of Apple’s disabled OpenMP support in Xcode compilers by automatically detecting Xcode versions, downloading compatible OpenMP runtimes, configuring compiler settings, and managing Makevars files. The implementation follows the official CRAN Mac maintainer recommendations while providing a user-friendly interface that eliminates manual configuration. This update enables compiled code scripts or local development on R packages like ranger, xgboost, and data.table to leverage parallel processing capabilities on macOS.

r
macos
openmp
hpc
parallel computing
Author

James Balamuta

Published

August 9, 2025

I’m excited to announce that {macrtools} (GitHub, Documentation) now includes OpenMP support for macOS! The new openmp_*() suite of functions makes parallel computing “just work” for R users on macOS.

The OpenMP Problem on macOS

If you’ve ever tried to install packages like ranger, xgboost, or data.table from source or compile a one-off OpenMP Rcpp script on macOS, you might have encountered this frustrating error:

clang: error: unsupported option '-fopenmp'

This happens because Apple has explicitly disabled OpenMP support in Xcode compilers, even though the underlying clang compiler has full OpenMP capabilities. Many R packages or Rcpp scripts that could benefit from parallel processing are left running single-threaded on macOS as a result.

This approach follows methodology from the official CRAN Mac maintainer and, thus, mirrors the note at the top of the page:

Warning! Everything described on this page is strictly experimental and not officially supported by Apple. It may break at any time. The information is provided in the hope of being useful to some tech-savvy people. It is not intended for the regular R user.

Meet the New openmp_*() Functions

The latest version of macrtools introduces five new functions that solve this problem elegantly:

# Check if OpenMP is installed
is_openmp_installed()

# Install OpenMP with automatic configuration  
openmp_install()

# Test your installation and configuration
openmp_test()

# Get version information
openmp_version()

# Clean uninstall with automatic cleanup
openmp_uninstall()

Automatic Everything

What makes this special is the automation. When you run:

openmp_install()

The function:

  1. Detects your Xcode version automatically
  2. Downloads the compatible OpenMP runtime from the official R-project repository
  3. Installs to system directories with proper permissions
  4. Configures your ~/.R/Makevars with the necessary compiler flags
  5. Verifies the installation works correctly

No manual configuration needed!

Compatible OpenMP runtime

One of the trickiest parts of OpenMP on macOS is version compatibility of the OpenMP run-time with the system toolchain. The openmp_install() function handles this by automatically mapping your Xcode version to the correct OpenMP runtime using the same compiler lookup approach recommended by the official CRAN Mac maintainer. For instance, we have:

  • Xcode 16.3+ → LLVM 19.1.0’s OpenMP
  • Xcode 16.0 - 16.2 → LLVM 17.0.6’s OpenMP
  • Xcode 15.x → LLVM 16.0.4’s OpenMP
  • And so on…

Aside: Historical Context

The need for this workaround emerged around R 4.0. From R 3.4 up to R 4.0, CRAN Mac binaries used a custom version of clang that included OpenMP support by default, making parallel computing relatively straightforward. Post-R 4.0, this approach was no longer viable, and the current “trick” of manually installing OpenMP runtime libraries became necessary.

While alternatives exist (like using homebrew’s LLVM with brew install llvm@17), they come with significant tradeoffs. Using a custom compiler means you can no longer use CRAN binaries as-is. Every R package would need to be compiled from source, significantly increasing installation time and complexity.

The macrtools approach provides a middle ground: it enables OpenMP support while maintaining compatibility with CRAN binaries for most packages.

Makevars Configuration Management

One other requirement for OpenMP to work is that R needs to know how to compile and link against the OpenMP runtime. This is done through the ~/.R/Makevars file, which contains compiler flags and linker settings. This file is unique to R and can be viewed as a variant of Make.

For OpenMP, R needs to tell the compiler to enable parallel processing (-Xclang -fopenmp) and link the OpenMP library (-lomp). macrtools uses a block-based management to safely add and remove OpenMP settings. This means you can have other custom compiler settings in your Makevars file without worrying about macrtools overwriting them and you can clearly see where the OpenMP settings are applied.

Here’s how it looks in your ~/.R/Makevars after running openmp_install():

# macrtools - OpenMP: start
CPPFLAGS += -Xclang -fopenmp
LDFLAGS += -lomp
# macrtools - OpenMP: end

A Note on Compatibility

While the OpenMP approach works well across most packages, some may have specific compatibility requirements. For example, data.table currently has issues with clang 17 (used in the latest Xcode CLI 16.3 [at the time of post]), requiring either:

  • Downgrading to Xcode CLI 16.2, or
  • Using LLVM from homebrew with brew install llvm@16

The macrtools functions handle the standard case, but complex compatibility issues may require manual intervention.

Looking Forward

This update represents a notable step toward accessible R development on macOS. While OpenMP remains experimental, the standardized approach provides a reliable pathway for users needing parallel computing. The openmp_*() functions join the existing gfortran_*() and xcode_cli_*() families to deliver automated toolchain management.