3709f14617
Device-side build artifacts need to be generated before compilation of the host side starts. Before the patch, the Makefile rules failed to meet that requirement, and things only worked when running make with a single job, i.e. with -j1. This patch tweaks the Makefile rules so that make is aware of the fact that the host side should only be compiled after the device side.
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
aarch64system libs to newly created host directory using sshfs. On host:
sudo sshfs -o nonempty,allow_other,default_permissions alice@192.168.x.y:/usr/lib/aarch64-linux-gnu/ /usr/lib/aarch64-linux-gnu/
- Mount cuda_host
aarch64CUDA libs to newly created host directory using sshfs. On host:
sudo sshfs -o nonempty,allow_other,default_permissions alice@192.168.x.y:/usr/local/cuda/targets/aarch64-linux/lib /usr/local/cuda/targets/aarch64-linux/lib
- Edit
examples/Makefile.includeto:
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 alice@192.168.x.y:~
- Move to cuda_host. On host:
ssh alice@192.168.x.y:~
./main