SYCL Academy, a set of learning materials for SYCL heterogeneous programming
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:
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. A playlist of the video content is also available.
| Lesson | Title | Slides | Video | Exercise | Source | Solution | ComputeCpp | DPC++ | hipSYCL | |--------|-------|--------|----------|--------|----------|------------|-------|---------|---------| | 1 | Introduction to SYCL | slides | video | exercise | NA | NA | Yes | Yes | Yes | | 2 | SYCL Topology Discover & Queue Creation | slides | video | exercise | source | solution | Yes | Yes | Yes | | 3 | SYCL Kernel Functions | slides | video | exercise | source | solution | Yes | Yes | Yes | | 4 | Managing Data in SYCL | slides | video | exercise | source | solution | Yes | Yes | Yes | | 5 | Data Dependencies in SYCL | slides | NA | NA | NA | NA | NA | NA | NA | | 6 | Handling SYCL Errors | slides | video | exercise | source | solution | Yes | Yes | Yes |
| Exercise | Title | Exercise | Source | Solution | ComputeCpp | DPC++ | hipSYCL | |--------|-------|--------|----------|--------|----------|------------|-------| | 1 | Image Grayscale | exercise | source | solution | Yes | Yes | Yes | | 2 | Matrix Transpose |exercise | source | solution | Yes | Yes | Yes | | 3 | Unified Shared Memory Extension (Optional) | exercise | source | solution | Yes | Yes | No |
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 2.0.0 | | 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 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 [email protected] +cudaIf you do not need the CUDA backend, you can remove
Of course, you can also build hipSYCL from source manually.
Before building the exercises you'll need:
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:
cmake ../ -G -A -D=ON
For/ we recommend:
sycl_implementationthis can be one of:
You can also specify the additional optional options:
Forwe recommend you specify the path to the root directory of your SYCL implementation installation, though this may not always be required.
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|cudaand, when compiling for GPU, the target architecture using
-DHIPSYCL_GPU_ARCH=arch. * For NVIDIA GPUs,
archis of the form
sm_XX. For example,
sm_60for Pascal GPUs (GeForce GTX 1000 series). * When compiling for AMD GPUs,
archis of the form
gfxXXX. For example,
gfx900for Vega 10 chips (Vega 56 and Vega 64) or
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.shwhich when is available in
/opt/intel/inteloneapiwhen installed as root or sudo and
On Windows the script is located in
Whereis wherever the
inteloneapidirectory is installed.
Once that's done you can invoke the DPC++ compiler as follows:
dpcpp -I/External/Catch2/single_include -o a.out source.cpp
Whereis 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.
In order to more easily deploy a SYCL implementation onto a bank of machines in a university lab for example, a Docker container can be used to deploy on these machines. This ensures all the dependencies that are needed are installed on each machine.
An example of how to set up a Docker container:
Now create a DockerFile that uses these packages, an example of how this might be done is below. Please note this file is not tested or maintained regularly but shows the elements that need to be installed.
RUN apt-get update RUN apt-get install -y git RUN apt-get install -y ninja-build RUN apt-get install -y g++ RUN apt-get install -y python3 RUN apt-get install -y python3-pip RUN apt-get install -y software-properties-common
RUN add-apt-repository -y ppa:ubuntu-toolchain-r/test RUN apt-get update
RUN python3 -m pip install cmake
install Intel OpenCL drivers from downloaded package
RUN tar -xvf l_opencl_p_18.1.0.015.tgz RUN cd l_opencl_p_18.1.0.015 RUN chmod +x install.sh RUN ./install.sh
Download the Khronos OpenCL headers
RUN git clone https://github.com/KhronosGroup/OpenCL-Headers.git RUN mv OpenCL-Headers/CL/ /opt/khronos/opencl/include
Set up the ICD Loader
RUN mkdir -p /etc/OpenCL/vendors/
&& echo "$OCL_LIB/libintelocl.so" > /etc/OpenCL/vendors/intel.icd
Create a directory for ComputeCpp
RUN mkdir /usr/local/computecpp RUN cd /usr/local/computecpp
Copy the ComputeCpp release package and extract it to /usr/local/computecpp
RUN cp Ubuntu-16.04-64bit.tar.gz . RUN tar -xvf Ubuntu-16.04-64bit.tar.gz
Add the ComputeCpp location to the path on the machine
ENV CC=gcc-8 ENV CXX=g++-8
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