This release has been tested on the following platforms:

• This release should work across a wide array of NVIDIA GPUs and CUDA versions, but Codeplay cannot guarantee correct operation on untested platforms.

• The package has been tested on Ubuntu 22.04 only, but can be installed on any Linux systems.

• This release of oneAPI for NVIDIA GPUs is not available for Windows, but a Windows package will be available in a future release.

• The plugin relies on CUDA being installed on your system. As CUDA no longer supports macOS®, a oneAPI for NVIDIA GPUs package is not available for macOS.

1. Install C++ development tools.

   You will need the following C++ development tools installed in order to build and run oneAPI applications: cmake, gcc, g++, make and pkg-config.

   The following console commands will install the above tools on the most popular Linux distributions:

   Ubuntu

   sudo apt update
   sudo apt -y install cmake pkg-config build-essential
   
   

   Red Hat and Fedora

   sudo yum update
   sudo yum -y install cmake pkgconfig
   sudo yum groupinstall "Development Tools"
   
   

   SUSE

   sudo zypper update
   sudo zypper --non-interactive install cmake pkg-config
   sudo zypper --non-interactive install pattern devel_C_C++
   
   

   Verify that the tools are installed by running:

   which cmake pkg-config make gcc g++
   
   

   You should see output similar to:

   /usr/bin/cmake
   /usr/bin/pkg-config
   /usr/bin/make
   /usr/bin/gcc
   /usr/bin/g++
   
   

2. Install an Intel® oneAPI Toolkit version 2024.0.2 that contains the DPC++/C++ Compiler.

   • For example, the “Intel oneAPI Base Toolkit” should suit most use cases.

   • The Toolkit must be version 2024.0.2 - otherwise oneAPI for NVIDIA GPUs cannot be installed.

3. Install the GPU driver and CUDA software stack for the NVIDIA GPU by following the steps described in the NVIDIA CUDA Installation Guide for Linux.

1. Download the latest oneAPI for NVIDIA GPUs (beta) installer:

   1. Directly via Website

   2. Using Download API with cURL or WGET (requires an account)

2. Run the downloaded self-extracting installer:

   sh oneapi-for-nvidia-gpus-2024.0.2-cuda-12.0-linux.sh

   • The installer will search for an existing Intel oneAPI Toolkit version 2024.0.2 installation in common locations. If you have installed an Intel oneAPI Toolkit in a custom location, use --install-dir /path/to/intel/oneapi.

   • If your Intel oneAPI Toolkit installation is outside your home directory, you may be required to run this command with elevated privileges, e.g. sudo.

1. To set up your oneAPI environment in your current session, source the Intel-provided setvars.sh script.

   For system-wide installations:

   . /opt/intel/oneapi/setvars.sh --include-intel-llvm
   
   

   For private installations (in the default location):

   . ~/intel/oneapi/setvars.sh --include-intel-llvm
   
   

   • The --include-intel-llvm option is required in order to add LLVM tools such as clang++ to the path.

   • Note that you will have to run this script in every new terminal session. For options to handle the setup automatically each session, see the relevant Intel oneAPI Toolkit documentation, such as Set Environment Variables for CLI Development

2. Ensure that the CUDA libraries and tools can be found in your environment.

   1. Run nvidia-smi - if it runs without any obvious errors in the output then your environment should be set up correctly.

   2. Otherwise, set your environment variables manually:

      export PATH=/PATH_TO_CUDA_ROOT/bin:$PATH
      export LD_LIBRARY_PATH=/PATH_TO_CUDA_ROOT/lib:$LD_LIBRARY_PATH
      
      

To verify the DPC++ CUDA plugin installation, the DPC++ sycl-ls tool can be used to make sure that SYCL now exposes the available NVIDIA GPUs. You should see something similar to the following in the sycl-ls output if NVIDIA GPUs are found:

[ext_oneapi_cuda:gpu:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.8 [CUDA 12.2]

• If the available NVIDIA GPUs are correctly listed, then the DPC++ CUDA plugin was correctly installed and set up.

• Otherwise, see the “Missing devices in sycl-ls output” section of the Troubleshooting documentation.

• Note that this command may also list other devices such as OpenCL™ devices, Intel GPUs, or AMD GPUs, based on the available hardware and DPC++ plugins installed.

1. Create a file simple-sycl-app.cpp with the following C++/SYCL code:

   #include <sycl/sycl.hpp>
   
   int main() {
     // Creating buffer of 4 ints to be used inside the kernel code
     sycl::buffer<int, 1> Buffer{4};
   
     // Creating SYCL queue
     sycl::queue Queue{};
   
     // Size of index space for kernel
     sycl::range<1> NumOfWorkItems{Buffer.size()};
   
     // Submitting command group(work) to queue
     Queue.submit([&](sycl::handler &cgh) {
       // Getting write only access to the buffer on a device
       auto Accessor = Buffer.get_access<sycl::access::mode::write>(cgh);
       // Executing kernel
       cgh.parallel_for<class FillBuffer>(
           NumOfWorkItems, [=](sycl::id<1> WIid) {
             // Fill buffer with indexes
             Accessor[WIid] = static_cast<int>(WIid.get(0));
           });
     });
   
     // Getting read only access to the buffer on the host.
     // Implicit barrier waiting for queue to complete the work.
     auto HostAccessor = Buffer.get_host_access();
   
     // Check the results
     bool MismatchFound{false};
     for (size_t I{0}; I < Buffer.size(); ++I) {
       if (HostAccessor[I] != I) {
         std::cout << "The result is incorrect for element: " << I
                   << " , expected: " << I << " , got: " << HostAccessor[I]
                   << std::endl;
         MismatchFound = true;
       }
     }
   
     if (!MismatchFound) {
       std::cout << "The results are correct!" << std::endl;
     }
   
     return MismatchFound;
   }
   
   

2. Compile the application with:

   icpx -fsycl -fsycl-targets=nvptx64-nvidia-cuda simple-sycl-app.cpp -o simple-sycl-app
   
   

   Depending on your CUDA version, you may see this warning, which can be safely ignored:

   icpx: warning: CUDA version is newer than the latest supported version 12.1 [-Wunknown-cuda-version]
   
   

3. Run the application with:

   ONEAPI_DEVICE_SELECTOR="ext_oneapi_cuda:*" SYCL_PI_TRACE=1 ./simple-sycl-app
   
   

   You should see output like:

   SYCL_PI_TRACE[basic]: Plugin found and successfully loaded: libpi_cuda.so [ PluginVersion: 14.37.1 ]
   SYCL_PI_TRACE[basic]: Plugin found and successfully loaded: libpi_unified_runtime.so [ PluginVersion: 14.37.1 ]
   SYCL_PI_TRACE[all]: Requested device_type: info::device_type::automatic
   SYCL_PI_TRACE[all]: Selected device: -> final score = 1500
   SYCL_PI_TRACE[all]:   platform: NVIDIA CUDA BACKEND
   SYCL_PI_TRACE[all]:   device: NVIDIA A100-PCIE-40GB
   The results are correct!
   
   

   If so, you have successfully set up and verified your oneAPI for NVIDIA GPUs development environment, and you can begin developing oneAPI applications.

   The rest of this document provides general information on compiling and running oneAPI applications on NVIDIA GPUs.

To compile a SYCL application for NVIDIA GPUs, use the icpx compiler provided with DPC++. For example:

icpx -fsycl -fsycl-targets=nvptx64-nvidia-cuda sycl-app.cpp -o sycl-app

The following flags are required:

• -fsycl: Instructs the compiler to build the C++ source file in SYCL mode. This flag will also implicitly enable C++17 and automatically link against the SYCL runtime library.

• -fsycl-targets=nvptx64-nvidia-cuda: Instructs the compiler to build SYCL kernels for the NVIDIA GPU target.

It is also possible to build the SYCL kernels for a specific NVIDIA architecture using the following flags:

• -Xsycl-target-backend=nvptx64-nvidia-cuda --cuda-gpu-arch=sm_80

Note that kernels are built for sm_50 by default, allowing them to work on a wider range of architectures, but limiting the usage of more recent CUDA features.

For more information on available SYCL compilation flags, see the DPC++ Compiler User’s Manual or for information on all DPC++ compiler options see the Compiler Options section of the Intel oneAPI DPC++/C++ Compiler Developer Guide and Reference.

The icpx compiler is by default a lot more aggressive with optimizations than the regular clang++ driver, as it uses both -O2 and -ffast-math. In many cases this can lead to better performance but it can also lead to some issues for certain applications. In such cases it is possible to disable -ffast-math by using -fno-fast-math and to change the optimization level by passing a different -O flag. It is also possible to directly use the clang++ driver which can be found in $releasedir/compiler/latest/linux/bin-llvm/clang++, to get regular clang++ behavior.

In addition to targeting NVIDIA GPUs, you can build SYCL applications that can be compiled once and then run on a range of hardware. The following example shows how to output a single binary including device code that can run on NVIDIA GPUs, AMD GPUs, or any device that supports SPIR e.g. Intel GPUs.

icpx -fsycl -fsycl-targets=amdgcn-amd-amdhsa,nvptx64-nvidia-cuda,spir64 \
        -Xsycl-target-backend=amdgcn-amd-amdhsa --offload-arch=gfx1030 \
        -Xsycl-target-backend=nvptx64-nvidia-cuda --offload-arch=sm_80 \
        -o sycl-app sycl-app.cpp

After compiling your SYCL application for an NVIDIA target, you should also ensure that the correct SYCL device representing the NVIDIA GPU is selected at runtime.

In general, simply using the default device selector should select one of the available NVIDIA GPUs. However in some scenarios, users may want to change their SYCL application to use a more precise SYCL device selector, such as the GPU selector, or even a custom selector.

The environment variable ONEAPI_DEVICE_SELECTOR may be used to help the SYCL device selectors by restricting the set of devices that can be used. For example, to only allow devices exposed by the DPC++ CUDA plugin:

export ONEAPI_DEVICE_SELECTOR="ext_oneapi_cuda:*"

For more details on this environment variable, see the Environment Variables section of the oneAPI DPC++ Compiler documentation. Note: this environment variable will be deprecated in a subsequent release.