This repository provides materials that can be used for teaching SYCL 1.2.1. The materials are provided using the "Creative Commons Attribution Share Alike 4.0 International" license.
If you're not familiar with SYCL or would like some further resources for learning about SYCL below are a list of useful resources:
- Read a description of SYCL on the Khronos website SYCL page.
- Go to the Khronos website to find a list of SYCL resources.
- Check out the SYCL 1.2.1 reference guide.
- Browse SYCL news, blog posts, videos, projects and more on the sycl.tech community website
- Get a list of the available SYCL implementations
To use these materials simply clone this repository.
The lectures are written in reveal.js, and can be found in "Lesson_Materials", in the sub-directory for each topic. To view them simply open the "index.html" file in your browser. Your browser will have a "Full Screen" mode that can be used to run the presentation, use the right and left cursors to move forward and backward in the presentation.
The exercises can be found in "Code_Exercises" in the sub-directory for each topic. Each exercise has a markdown document instructing what to do in the exercise, a source file to start with and a solution file to provide an example implementation to compare against.
Contributions to the materials are very gratefully received and this can be done by submitting a Pull Request with any changes. Please limit the scope of each Pull Request so that they can be reviewed and merged in a timely manner.
Codeplay Software Ltd., Heidelberg University.
Abertay University, Universidad de Concepcion, TU Dresden, University of Edinburgh, Federal University of Sao Carlos, University of Glasgow, Heriot Watt University, Universitat Innsbruck, Universidad de Málaga, University of Salerno and University of the West of Scotland.
The SYCL Academy curriculum is divided up into a number of short lessons consisting of slides for presenting the material and a more detailed write-up, each accompanied by a tutorial for getting hands on experience with the subject matter.
Each of the lessons are designed to be self contained modules in order to support both academic and training style teaching environments.
The exercises can be built for ComputeCpp CE, DPC++ and hipSYCL.
Below is the supported platforms and devices for each SYCL implementations, see this before deciding which SYCL implementation to use.
Make sure to also install the specified version to ensure that you can build all of the exercises.
| Implementation | Supported Platforms | Supported Devices | Required Version |
|---|---|---|---|
| ComputeCpp | Windows 10 Visual Studio 2019 (64bit) Ubtuntu 18.04 (64bit) |
Intel CPU (OpenCL) Intel GPU (OpenCL) |
CE 2.2.0 |
| DPC++ | Intel DevCloud Windows 10 Visual Studio 2019 (64bit) Ubtuntu 18.04 (64bit) |
Intel CPU (OpenCL) Intel GPU (OpenCL) Intel FPGA (OpenCL) Nvidia GPU (CUDA) |
2021.1-beta05 |
| hipSYCL | Any Linux | CPU (OpenMP) AMD GPU (ROCm)* Nvidia GPU (CUDA) |
Latest master |
* See here for the official list of GPUs supported by AMD for ROCm. We do not recommend using GPUs earlier than gfx9 (Vega 10 and Vega 20 chips).
First you'll need to install your chosen SYCL implementation and any dependencies they require.
To set up ComputeCpp download the ComputeCpp CE package and follow the getting started instructions.
To set up DPC++ follow the getting started instructions.
If you are using the Intel DevCloud then the latest version of DPC++ will already be installed and available in the path.
You will need a hipSYCL build from April 26th 2020 or newer. The easiest way to install a recent distribution of hipSYCL is to use the daily package repositories. Binary packages are provided for Ubuntu 18.04, CentOS 7 and Arch Linux. See here for an explanation of the packages that you need.
If you do not need the ROCm backend, a recent distribution can also be installed using the spack package manager:
spack install hipsycl@master +cuda
If you do not need the CUDA backend, you can remove +cuda.
Of course, you can also build hipSYCL from source manually.
Before building the exercises you'll need:
- One of the platforms in the support matrix above, depending on which SYCL implementation you are wishing to build for.
- A C++17 or above tool-chain.
- An appropriate build system for the platform you are targeting (CMake, Ninja, Make, Visual Studio).
Clone this repository, there are some additional dependencies configured as git sub-modules so make sure to clone those as well. Then simply invoke CMake as follows:
mkdir build
cd build
cmake ../ -G<cmake_generator> -A<cmake_arch> -D<sycl_implementation>=ON
For <cmake_generator> / <cmake_arch> we recommend:
- Visual Studio 16 2019 / x64 (Windows)
- Ninja / NA (Windows or Linux)
- Make / NA (Linux)
For sycl_implementation this can be one of:
SYCL_ACADEMY_USE_COMPUTECPPSYCL_ACADEMY_USE_HIPSYCL
You can also specify the additional optional options:
-DSYCL_ACADEMY_INSTALL_ROOT=<path_to_sycl_impl_install_root>
For <path_to_sycl_impl_install_root> we recommend you specify the path to the
root directory of your SYCL implementation installation, though this may not
always be required.
-DSYCL_ACADEMY_ENABLE_SOLUTIONS=ON
This will enable building the solutions for each exercise as well as the source files. This is disabled by default.
When building with hipSYCL, cmake will additionally require you to specify the
target platform using -DHIPSYCL_PLATFORM=cpu|rocm|cuda and, when compiling for
GPU, the target architecture using -DHIPSYCL_GPU_ARCH=arch.
- For NVIDIA GPUs,
archis of the formsm_XX. For example,sm_60for Pascal GPUs (GeForce GTX 1000 series). - When compiling for AMD GPUs,
archis of the formgfxXXX. For example,gfx900for Vega 10 chips (Vega 56 and Vega 64) orgfx906(Radeon VII).
If you are using DPC++ there is no CMake integration, but it is very simple to use the DPC++ compiler directly.
First you have to ensure that your environment is configured to use DPC++ (note if you are using the Intel DevCloud then you don't need to do this step).
On Linux simply call the setvars.sh which when is available in
/opt/intel/inteloneapi when installed as root or sudo and
~/intel/inteloneapi/ otherwise.
source /opt/intel/inteloneapi/setvars.sh
or
source ~/intel/inteloneapi/setvars.sh
On Windows the script is located in <dpc++_install_root>\setvars.bat
Where <dpc++_install_root> is wherever the inteloneapi directory is
installed.
Once that's done you can invoke the DPC++ compiler as follows:
dpcpp -I<syclacademy_root>/External/Catch2/single_include -o a.out source.cpp
Where <syclacademy_root> is the path to the root directory of where you cloned
this repository.
Hosted by tech.io, this SYCL Introduction tutorial introduces the concepts of SYCL. The website also provides the ability to compile and execute SYCL code from your web browser.
ComputeCpp, a SYCL v1.2.1 conformant implementation by Codeplay Software provides setup instructions on developer.codeplay.com. There is more detailed information about what hardware is supported by ComputeCpp on the Platform Support page.
Other SYCL implementations can be found on the SYCL community website sycl.tech.
SYCL and the SYCL logo are trademarks of the Khronos Group Inc.