GNAT for CUDA

GNAT for CUDA® is a toolsuite that allows to compile Ada and SPARK code directly for NVIDIA GPUs.

Documentation

For a thorough discussion about GNAT for CUDA please consult the official documentation. A html version can be built like so:

cd doc
make hmtl

Status

Beta

Quickstart

End user - archive distribution

Note: During beta phase this package is available on request/invitation only.

Prerequisites

• GNAT toolchain - AdaCore client portal
• CUDA libraries - eg. apt-get
• GNAT aarch64-linux cross compiler toolchain - AdaCore client portal (optional)

Developper - git repository clone

Note: During beta phase this repo can only be built by AdaCore engineers with anod acces.

Prerequisites

• GNAT toolchain - anod
• CUDA libraries - eg. apt-get
• GNAT aarch64-linux cross compiler toolchain - anod (optional)
• GNAT sources - anod (set root Makefile $GNAT_SRC to it)
• bb-runtimes - anod (set root Makefile $BB_SRC to it)
• CUDA_env - anod (set to system environment variables)

Setup GNAT for CUDA

chmod +x setup.sh
./setup.sh

• End user only:

source ./env.sh 

Build cuda-gcc (optional)

• First Setup GNAT for CUDA. Then:

make

Compilation of vectorAdd example program

Note: By default we are building for x86_64, Turing family GPU sm_75

• First Setup GNAT for CUDA. Then:

cd examples/0_Simple/vectorAdd
make

Cross-compilation of vectorAdd example program

Note: To illustrate concrete cross-compilation steps, the following instructions are contextualized for cross-compiling from a x86_64-linux desktop (host) to a aarch64-linux Jetson Nano (cuda_host) Maxwell family GPU sm_53 running an Ubuntu 18.04 derivative as officialy published by NVIDIA. The cuda_host is located at LAN IP address 192.168.x.y running as user alice:

• First Setup GNAT for CUDA.

• Make sure your cross toolchain is properly installed.

$ which aarch64-linux-gnu-gcc
[somewhere_on_your_disk]/aarch64-linux-linux64/gnat/install/bin/aarch64-linux-gnu-gcc

$ echo $ENV_PREFIX
[somewhere_on_your_disk]/aarch64-linux-linux64/system-libs/src/aarch64-linux-system

• As we will use the CUDA libraries found on the cuda_host, install sshfs. On host:

sudo apt install sshfs

• Create folders. On host:
  /usr/lib/aarch64-linux-gnu/
/usr/local/cuda/targets/aarch64-linux/lib
/usr/local/cuda/targets/aarch64-linux/lib/stubs

sudo mkdir -p /usr/lib/aarch64-linux-gnu/
sudo mkdir -p /usr/local/cuda/targets/aarch64-linux/lib/stubs

• Mount cuda_host aarch64 system libs to newly created host directory using sshfs. On host:

sudo sshfs -o nonempty,allow_other,default_permissions [email protected]:/usr/lib/aarch64-linux-gnu/ /usr/lib/aarch64-linux-gnu/

• Mount cuda_host aarch64 CUDA libs to newly created host directory using sshfs. On host:

sudo sshfs -o nonempty,allow_other,default_permissions [email protected]:/usr/local/cuda/targets/aarch64-linux/lib /usr/local/cuda/targets/aarch64-linux/lib

• Edit examples/Makefile.include to:

GPU_ARCH=sm_53
CUDA_HOST=aarch64-linux

• Build example program. On host:

source ./env.sh
cd examples/0_Simple/vectorAdd
make

• Copy executable to cuda_host. On host:

scp main [email protected]:~

• Move to cuda_host. On host:

ssh [email protected]:~
./main