This release should work across a wide array of AMD GPUs, but Codeplay cannot guarantee correct operation on untested platforms.

The following platforms should work:

This release has been tested on the following platforms (using the upstream AMD GPU driver in the Linux kernel):

• In some cases the package for a given HIP version may work with future minor versions, however Codeplay cannot guarantee that anything other than the tested HIP releases will work.

• Each of the tested versions can be installed alongside another existing HIP installation. See the Multi-version ROCm Installation section of the ROCm Installation Guide.

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 2025.0.0, that contains the DPC++/C++ Compiler.

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

   • The Toolkit must be version 2025.0.0 - otherwise oneAPI for AMD GPUs cannot be installed.

3. Install the GPU driver and ROCm™ software stack for the AMD GPU.

   • For example, for ROCm 5.4.3, follow the steps described in the Installation with install script guide.

   • Using the amdgpu-install installer is recommended, with the --usecase="dkms,graphics,opencl,hip,hiplibsdk" argument to ensure that all required components are installed.

1. Download the latest oneAPI for AMD GPUs installer from our website.

2. Run the downloaded self-extracting installer:

   sh oneapi-for-amd-gpus-2025.0.0-rocm-5.4.3-linux.sh

   • The installer will search for an existing Intel oneAPI Toolkit version 2025.0.0 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 HIP libraries and tools can be found in your environment:

   1. Run rocminfo - 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_ROCM_ROOT/bin:$PATH
      export LD_LIBRARY_PATH=/PATH_TO_ROCM_ROOT/lib:$LD_LIBRARY_PATH
      
      

      ROCm is commonly installed in /opt/rocm-x.x.x/.

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

[hip:gpu][hip:0] AMD HIP BACKEND, AMD Radeon PRO W6800 gfx1030 [HIP 60140.9]

• If the available AMD GPUs are correctly listed, then the DPC++ HIP 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 NVIDIA® 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=amdgcn-amd-amdhsa \
           -Xsycl-target-backend --offload-arch=<ARCH> \
           -o simple-sycl-app simple-sycl-app.cpp
   
   

   Where ARCH is the GPU architecture e.g. gfx1030, which you can check by running:

   rocminfo | grep 'Name: *gfx.*'
   
   

   You should see the GPU architecture in the output, for example:

     Name:                    gfx1030
   
   

3. Run the application with:

   ONEAPI_DEVICE_SELECTOR="hip:*" SYCL_UR_TRACE=1 ./simple-sycl-app
   
   

   You should see output like:

   <LOADER>[INFO]: loaded adapter 0x0x43c050 (libur_adapter_hip.so.0)
   SYCL_UR_TRACE: Requested device_type: info::device_type::automatic
   SYCL_UR_TRACE: Selected device: -> final score = 1500
   SYCL_UR_TRACE:   platform: AMD HIP BACKEND
   SYCL_UR_TRACE:   device: AMD Instinct MI210
   The results are correct!
   
   

   If so, you have successfully set up and verified your oneAPI for AMD 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 AMD GPUs.

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

icpx -fsycl -fsycl-targets=amdgcn-amd-amdhsa \
        -Xsycl-target-backend=amdgcn-amd-amdhsa --offload-arch=gfx1030 \
        -o sycl-app sycl-app.cpp

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=amdgcn-amd-amdhsa: Instructs the compiler to build SYCL kernels for the AMD GPU target.

• -Xsycl-target-backend=amdgcn-amd-amdhsa --offload-arch=gfx1030: Instructs the compiler to build SYCL kernels for the gfx1030 AMD GPU architecture.

Note that when targeting an AMD GPU, the specific architecture of the GPU must be provided.

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 AMD 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 AMD GPUs, NVIDIA GPUs, or any device that supports SPIR e.g. Intel GPUs.

icpx -fsycl -fsycl-targets=spir64,amdgcn-amd-amdhsa,nvptx64-nvidia-cuda \
        -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

The above command can be broken down into the following ingredients:

• -fsycl tells the compiler to compile SYCL device code in addition to host code.

• -fsycl-targets=spir64,amdgcn-amd-amdhsa,nvptx64-nvidia-cuda specifies three targets for SYCL device code. The targets may be generic and produce code which will be further compiled just-in-time (JIT) during execution for a specific GPU architecture (Intel and NVIDIA targets only). Further flags may be added to generate architecture-specific device code ahead-of-time (AOT) in addition to the generic code, as follows.

• -Xsycl-target-backend=amdgcn-amd-amdhsa tells the flag parser that the following flag should be passed only to the compiler backend for the amdgcn-amd-amdhsa target and no other targets. Specifying -Xsycl-target-backend without any value would pass the following flag to the compiler backend for all SYCL device targets.

• --offload-arch=gfx1030 specifies the AMD GPU architecture gfx1030 for the AOT compilation.

• -Xsycl-target-backend=nvptx64-nvidia-cuda tells the flag parser that the following flag should be passed only to the compiler backend for the nvptx64-nvidia-cuda target.

• --offload-arch=sm_80 specifies the NVIDIA GPU architecture (compute capability) sm_80 for the AOT compilation.

A binary compiled in the above way can successfully run the SYCL kernels on:

• Intel CPUs and GPUs with JIT compilation of the SPIR code

• AMD GPUs with architecture gfx1030 directly from the binary without any JIT compilation

• NVIDIA GPUs with compute capability 8.0 directly from the binary without any JIT compilation

• other supported NVIDIA GPUs with JIT compilation

AOT compilation for Intel hardware is also possible in combination with AMD and NVIDIA targets, and can be achieved by using the spir64_gen target and the corresponding architecture flags. For example, to compile the above application AOT for the Ponte Vecchio Intel graphics architecture, the following command can be used:

icpx -fsycl -fsycl-targets=spir64_gen,amdgcn-amd-amdhsa,nvptx64-nvidia-cuda \
        -Xsycl-target-backend=spir64_gen '-device pvc' \
        -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

Note the different syntax -device <arch> as compared to --offload-arch=<arch> which is required due to a different compiler toolchain being used for the Intel targets.

The compiler driver also offers alias targets for each target+architecture pair to make the command line shorter and easier to understand for humans. Thanks to the aliases, the -Xsycl-target-backend flags no longer need to be specified. The above command is equivalent to:

icpx -fsycl -fsycl-targets=intel_gpu_pvc,amd_gpu_gfx1030,nvidia_gpu_sm_80 \
        -o sycl-app sycl-app.cpp

The full list of aliases is documented in the DPC++ Compiler User’s Manual.

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

In general, simply using the default device selector should select one of the available AMD 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.

export ONEAPI_DEVICE_SELECTOR="hip:*"

export ONEAPI_DEVICE_SELECTOR="hip:1,3"

    std::vector<sycl::device> Devs;
    for (const auto &plt : sycl::platform::get_platforms()) {
      if (plt.get_backend() == sycl::backend::ext_oneapi_hip) {}
        Devs=plt.get_devices();
        break;
      }
    }

export HIP_VISIBLE_DEVICES=1,3

    std::vector<sycl::device> Devs =
      sycl::device::get_devices(sycl::info::device_type::gpu);