ARM (Linux)

Julia fully supports ARMv8 (AArch64) processors, and supports ARMv7 and ARMv6 (AArch32) with some caveats. This file provides general guidelines for compilation, in addition to instructions for specific devices.

A list of known issues for ARM is available. If you encounter difficulties, please create an issue including the output from cat /proc/cpuinfo.

32-bit (ARMv6, ARMv7)

Julia has been successfully compiled on several variants of the following ARMv6 & ARMv7 devices:

ARMv7 / Cortex A15 Samsung Chromebooks running Ubuntu Linux under Crouton;
Raspberry Pi.
Odroid.

Julia requires at least the armv6 and vfpv2 instruction sets. It's recommended to use armv7-a. armv5 or soft float are not supported.

Raspberry Pi 1 / Raspberry Pi Zero

If the type of ARM CPU used in the Raspberry Pi is not detected by LLVM, then explicitly set the CPU target by adding the following to Make.user:

JULIA_CPU_TARGET=arm1176jzf-s

To complete the build, you may need to increase the swap file size. To do so, edit /etc/dphys-swapfile, changing the line:

CONF_SWAPSIZE=100

to:

CONF_SWAPSIZE=512

before restarting the swapfile service:

sudo /etc/init.d/dphys-swapfile stop
sudo /etc/init.d/dphys-swapfile start

Raspberry Pi 2

The type of ARM CPU used in the Raspberry Pi 2 is not detected by LLVM. Explicitly set the CPU target by adding the following to Make.user:

JULIA_CPU_TARGET=cortex-a7

Depending on the exact compiler and distribution, there might be a build failure due to unsupported inline assembly. In that case, add MCPU=armv7-a to Make.user.

AArch64 (ARMv8)

Julia has been successfully built on the following ARMv8 devices:

Compilation on ARMv8-A requires that Make.user is configured as follows:

MCPU=armv8-a

Starting from Julia v1.10, JITLink is automatically enabled on this architecture for all operating systems when linking to LLVM 15 or later versions. Due to a bug in LLVM memory manager, non-trivial workloads may generate too many memory mappings that on Linux can exceed the limit of memory mappings (mmap) set in the file /proc/sys/vm/max_map_count, resulting in an error like

JIT session error: Cannot allocate memory

Should this happen, ask your system administrator to increase the limit of memory mappings for example with the command

sysctl -w vm.max_map_count=262144

nVidia Jetson TX2

Julia builds and runs on the nVidia Jetson TX2 platform with minimal configuration changes.

After configuring Make.user as per the AArch64 instructions in this document, follow the general build instructions. The majority of the build dependencies specified in the instructions are installed by the default configuration flashed by Jetpack 3.0. The remaining tools can be installed by issuing the following command:

sudo apt-get install gfortran wget cmake

A full parallel build, including LLVM, will complete in around two hours. All tests pass and CUDA functionality is available through, e.g., CUDAdrv.