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SYCL Academy, a set of learning materials for SYCL heterogeneous programming

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SYCL Academy

SYCL Academy

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.

What is SYCL?

If you're not familiar with SYCL or would like some further resources for learning about SYCL below are a list of useful resources:

How to use the Materials

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.

Contributing to SYCL Academy

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.

List of Contributors

Codeplay Software Ltd., Heidelberg University.

Supporting Organizations

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.

Lesson Curriculum

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 |

Additional Exercises

| 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 |

Building the Exercises

The exercises can be built for ComputeCpp CE, DPC++ and hipSYCL.

Supported Platforms

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)
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).

Install SYCL implementations

First you'll need to install your chosen SYCL implementation and any dependencies they require.

Installing ComputeCpp

To set up ComputeCpp download the ComputeCpp CE package and follow the getting started instructions.

Installing DPC++

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.

Installing hipSYCL

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] +cuda
If 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:

  • 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).

Configuring using CMake (ComputeCpp CE and hipSYCL only)

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 -A -D=ON


 we recommend:

  • Visual Studio 16 2019 / x64 (Windows)
  • Ninja / NA (Windows or Linux)
  • Make / NA (Linux)


this can be one of:

You can also specify the additional optional options:



 we 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.

Additional cmake arguments for hipSYCL

When building with hipSYCL, cmake will additionally require you to specify the target platform using

and, when compiling for GPU, the target architecture using
. * For NVIDIA GPUs,
is of the form
. For example,
for Pascal GPUs (GeForce GTX 1000 series). * When compiling for AMD GPUs,
is of the form
. For example,
for Vega 10 chips (Vega 56 and Vega 64) or
(Radeon VII).

Compiling directly (DPC++ only)

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
which when is available in
when installed as root or sudo and

source /opt/intel/inteloneapi/


source ~/intel/inteloneapi/

On Windows the script is located in



 is wherever the 
directory 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


 is the path to the root directory of where you cloned
this repository.

Online Interactive Tutorial

Hosted by, 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.

Setting up Computers for SYCL

Machine Setup Instructions

ComputeCpp, a SYCL v1.2.1 conformant implementation by Codeplay Software provides setup instructions on 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

Setting up a Docker Container for Lab Machines

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:

For Intel CPU or GPU * Download the OpenCL Drivers for Intel from the Intel website * Download the latest ComputeCpp release package from

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.

FROM ubuntu:18.04

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 RUN ./

Download the Khronos OpenCL headers

RUN git clone RUN mv OpenCL-Headers/CL/ /opt/khronos/opencl/include

Set up the ICD Loader

RUN mkdir -p /etc/OpenCL/vendors/
&& echo "$OCL_LIB/" > /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 PATH=OCL_INC:OCL_LIB:/usr/local/computecpp/bin:/usr/local/computecpp/include:/usr/local/computecpp/lib:${PATH}

ENV CC=gcc-8 ENV CXX=g++-8

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