At WWDC 2020 earlier this year, Apple announced a transition from Intel to ARM-based processors in their laptops. This blog is about the prospects of when R will work on that platform, based on experimentation on a developer machine running A12Z, one of the “Apple silicon” processors.
The new platform will include Rosetta 2, a dynamic translation framework which runs binaries built for 64-bit Intel Macs using just-in-time, dynamic translation of binary code. The good news is that R seems to be working fine with the dynamic translation, so R users don’t need to worry even if they use current releases. However, the interesting question is whether R will also work natively. Native execution is expected to be faster, and the transition probably has to be done eventually, anyway.
The executive summary is: while there is currently no released Fortran compiler for the platform, a development version of GNU Fortran already seems to be working fine. There are some surprising results with NaN payload propagation leading to unexpected results when computing with numeric NAs, but these can be overcome by changing the mode of the floating-point unit, which has already been done in R-devel. More details follow in this post.
R needs a Fortran 90 compiler
R needs at least a Fortran 90 compiler to build. Most of the Fortran code
in R and base and recommended packages is still in Fortran 77, so it can be
f2c to C and compiled by a C compiler. However, some code
already uses Fortran 90 features and back-porting that would require a
In addition, R ships with a slightly modified version of reference LAPACK and BLAS which need a Fortran 90 compiler to build. It is highly preferable to have reference LAPACK/BLAS available also on the new platform, even though Apple provides an optimized version of BLAS and LAPACK as part of the Accelerate framework in macOS.
GCC’s GFortran supports 64-bit ARMs: an earlier blog post from May was about building and testing R on Linux running on 64-bit ARM (Aarch64) inside QEMU emulator. However, the Apple silicon platform uses a different application binary interface (ABI) which GFortran does not support, yet.
Currently, there seems to be no other Fortran 90 compiler, neither free nor commercial. Specifically, LLVM’s Fortran compiler (now called Flang again) is not yet finished.
Building development version of GCC/GFortran
While GFortran does not support Apple silicon yet (neither any release nor the GCC trunk), there is a private development branch of GCC including GFortran by Iain Sandoe, which we experimented with.
Building GCC from source as usual requires first building also GMP, MPFR and
MPC for the platform. GMP (version 6.2.0) required back-porting a patch
from the trunk (configure script for Apple silicon, assembler macros).
Re-generating the configure script and make files natively also required
building libtool. The configure script for GMP had to be explicitly run
--build=aarch64-apple-darwin20.0.0, even when building natively. The
configure script for GCC was run with
--with-sysroot, specifying a
MacOSX.sdk installed via
R requires a number of dependencies, which can be built natively following R Installation and Administration, using the Apple/LLVM toolchain provided by Apple (Fortran compiler is not needed). We have also compiled a native build of Subversion, even though in principle one could build R from a tarball.
R and recommended packages were built using Apple/LLVM clang to compile C
CFLAGS=-Wno-error=implicit-function-declaration) and Objective C and
using the development version of GFortran to compile Fortran code.
A number of tests for R and recommended packages have failed for a
platform-specific reason, but it turned out that all for the same reason:
surprising propagation of NaN payload, where e.g.
NA * 1 is
NA/NaN payload propagation
R’s NA for floating point numbers is represented using NaN with a special payload value. NaNs that originate from computations not involving NA have a different (e.g. zero) payload, so can be distinguished from NA. NaNs are often passed to computations inside R without explicit checks and the same happens inside package code and external numerical code, which have no idea about R’s NA concept nor representation.
The IEEE 754 standard for floating point arithmetics does not mandate how NaN payloads should be propagated through computations. The result of computations involving NAs and/or other NaNs depends on the CPU/floating point unit, on compiler optimizations (compiler may re-order computations), and on the algorithm (e.g. it is tempting to ignore input values not needed to compute the result under the assumption they are finite, but without actually checking they are finite).
More information can be found in R’s online help (
disclaimers that the difference between NA and NaN should not be relied on
(citing in the wording of recent R-devel):
Computations involving ‘NaN’ will return ‘NaN’ or perhaps ‘NA’: which of those two is not guaranteed and may depend on the R platform (since compilers may re-order computations).
Numerical computations using ‘NA’ will normally result in ‘NA’: a possible exception is where ‘NaN’ is also involved, in which case either might result (which may depend on the R platform). However, this is not guaranteed and future CPUs and/or compilers may behave differently.
Intel FPUs worked relatively well with respect to R NAs: binary operations with
NAs resulted in NAs, even when NaNs were involved (based on experimentation).
However, currently R on Intel machines is typically built to use SSE
instructions for computations, which do not work that well for R NAs: binary
operations with NAs only result in NA when the other argument is not a NaN, or
when the NA is the first, so
NaN + NA is
NA + NaN is
64-bit Intel with SSE has most likely been the prevailing setup for R recently,
tests have already been updated to accept this behavior.
However, it turns out that on A12Z, R’s NA becomes a normal NaN after any
binary operation (the payload is lost), so even
NA * 1 is
NaN. This is
within what has been warned against in the R documentation cited above, but
still a number of tests for R and recommended packages capture the
(documented as unreliable) behavior expecting that operations like this
NA. We have not investigated how many of other CRAN/BIOC
The ARM architecture floating point units (VFP, NEON) support RunFast mode,
which includes flush-to-zero and default NaN. The latter means that payload
of NaN operands is not propagated, all result NaNs have the default payload,
so in R, even
NA * 1 is
NaN. Luckily, RunFast mode can be disabled, and
when it is, the NaN payload propagation is friendlier to R NAs than with
Intel SSE (
NaN + NA is
NA). We have therefore updated R to disable
RunFast mode on ARM on startup, which resolved all the issues observed.
We have not run into this issue earlier as RunFast has been disabled by default on other platforms, including Raspberry Pi (tested on an old model 2 with 32-bit ARM, BCM2835) and on QEMU emulating Aarch64 (64-bit ARM, Cortex-A72).
It turns out there is hope that R will work on Apple silicon. A usable Fortran
90 compiler for Apple silicon will hopefully be available relatively soon, since
the development version of GFortran already seems to be working (
passed for R including reference LAPACK/BLAS) and there is a strong need for
such compiler not only for R, but any scientific computing on that platform.
Any package native code that wants to reliably preserve NAs (computations with at least one NA value on input provide NA on output) has to include explicit checks, be it for computations implemented in the package native code or in external libraries. That is the only portable, reliable way, and has been the only one for long time. Packages that choose to not guarantee such propagation, on the other hand, should not capture in tests the coincidental propagation on the developer’s platform. On ARM, and hence also Apple silicon, R now masks some of these issues by disabling the RunFast mode, but another new platform may appear where this won’t be possible, and more importantly, NAs may be “lost” also due to compiler optimizations or algorithmically in external libraries.
Apple Silicon ABI. Specifics of the application binary interface (ABI) on Apple silicon.
GCC Bugzilla Enhancement/bug report with updates on development of GCC/GFortran support for Apple silicon.
Experimental GCC for Apple Silicon Development branch of GCC with support for Apple silicon by Iain Sandoe.
GMP Support for Apple Silicon. GMP Support for Apple silicon by Torbjorn Granlund.
Floating point exception tracking and NAN propagation A text also on NaN payload propagation by Agner Fog.
ARM RunFast Mode (https://developer.arm.com/documentation/ddi0274/h/programmer-s-model/compliance-with-the-ieee-754-standard/ieee-754-standard-implementation-choices) Description of differences of RunFast mode from IEEE 754.