Support, Getting Involved, and FAQ

Please do not hesitate to reach out to us via or join one of our regular calls. Some common questions are answered in the FAQ.


OpenMP in LLVM Technical Call

  • Development updates on OpenMP (and OpenACC) in the LLVM Project, including Clang, optimization, and runtime work.
  • Join OpenMP in LLVM Technical Call.
  • Time: Weekly call on every Wednesday 7:00 AM Pacific time.
  • Meeting minutes are here.
  • Status tracking page.

OpenMP in Flang Technical Call

  • Development updates on OpenMP and OpenACC in the Flang Project.
  • Join OpenMP in Flang Technical Call
  • Time: Weekly call on every Thursdays 8:00 AM Pacific time.
  • Meeting minutes are here.
  • Status tracking page.



The FAQ is a work in progress and most of the expected content is not yet available. While you can expect changes, we always welcome feedback and additions. Please contact, e.g., through

Q: How to contribute a patch to the webpage or any other part?

All patches go through the regular LLVM review process.

Q: How to build an OpenMP GPU offload capable compiler?

To build an effective OpenMP offload capable compiler, only one extra CMake option, LLVM_ENABLE_RUNTIMES=”openmp”, is needed when building LLVM (Generic information about building LLVM is available here.). Make sure all backends that are targeted by OpenMP to be enabled. By default, Clang will be built with all backends enabled.

For Nvidia offload, please see _build_nvidia_offload_capable_compiler. For AMDGPU offload, please see _build_amdgpu_offload_capable_compiler.


The compiler that generates the offload code should be the same (version) as the compiler that builds the OpenMP device runtimes. The OpenMP host runtime can be built by a different compiler.

Q: How to build an OpenMP NVidia offload capable compiler?

The Cuda SDK is required on the machine that will execute the openmp application.

If your build machine is not the target machine or automatic detection of the available GPUs failed, you should also set:

  • CLANG_OPENMP_NVPTX_DEFAULT_ARCH=sm_XX where XX is the architecture of your GPU, e.g, 80.
  • LIBOMPTARGET_NVPTX_COMPUTE_CAPABILITIES=YY where YY is the numeric compute capacity of your GPU, e.g., 75.

Q: How to build an OpenMP AMDGPU offload capable compiler?

A subset of the ROCm <> toolchain is required to build the LLVM toolchain and to execute the openmp application. Either install ROCm somewhere that cmake’s find_package can locate it, or build the required subcomponents ROCt and ROCr from source.

The two components used are ROCT-Thunk-Interface, roct, and ROCR-Runtime, rocr. Roct is the userspace part of the linux driver. It calls into the driver which ships with the linux kernel. It is an implementation detail of Rocr from OpenMP’s perspective. Rocr is an implementation of HSA <>.

SOURCE_DIR=same-as-llvm-source # e.g. the checkout of llvm-project, next to openmp BUILD_DIR=somewhere INSTALL_PREFIX=same-as-llvm-install

cd $SOURCE_DIR git clone -b roc-4.1.x –single-branch git clone -b rocm-4.1.x –single-branch

cd $BUILD_DIR && mkdir roct && cd roct cmake $SOURCE_DIR/ROCT-Thunk-Interface/ -DCMAKE_INSTALL_PREFIX=$INSTALL_PREFIX -DCMAKE_BUILD_TYPE=Release -DBUILD_SHARED_LIBS=OFF make && make install


IMAGE_SUPPORT requires building rocr with clang and is not used by openmp.

Provided cmake’s find_package can find the ROCR-Runtime package, LLVM will build a tool bin/amdgpu-arch which will print a string like ‘gfx906’ when run if it recognises a GPU on the local system. LLVM will also build a shared library,, which is linked against rocr.

With those libraries installed, then LLVM build and installed, try:

clang -O2 -fopenmp -fopenmp-targets=amdgcn-amd-amdhsa example.c -o example && ./example

Q: What are the known limitations of OpenMP AMDGPU offload?

LD_LIBRARY_PATH is presently required to find the openmp libraries.

There is no libc. That is, malloc and printf do not exist. Also no libm, so functions like cos(double) will not work from target regions.

Cards from the gfx10 line, ‘navi’, that use wave32 are not yet implemented.

Some versions of the driver for the radeon vii (gfx906) will error unless the environment variable ‘export HSA_IGNORE_SRAMECC_MISREPORT=1’ is set.

It is a recent addition to LLVM and the implementation differs from that which has been shipping in ROCm and AOMP for some time. Early adopters will encounter bugs.

Q: Does OpenMP offloading support work in pre-packaged LLVM releases?

For now, the answer is most likely no. Please see Q: How to build an OpenMP GPU offload capable compiler?.

Q: Does OpenMP offloading support work in packages distributed as part of my OS?

For now, the answer is most likely no. Please see Q: How to build an OpenMP GPU offload capable compiler?.

Q: Does Clang support <math.h> and <complex.h> operations in OpenMP target on GPUs?

Yes, LLVM/Clang allows math functions and complex arithmetic inside of OpenMP target regions that are compiled for GPUs.

Clang provides a set of wrapper headers that are found first when math.h and complex.h, for C, cmath and complex, for C++, or similar headers are included by the application. These wrappers will eventually include the system version of the corresponding header file after setting up a target device specific environment. The fact that the system header is included is important because they differ based on the architecture and operating system and may contain preprocessor, variable, and function definitions that need to be available in the target region regardless of the targeted device architecture. However, various functions may require specialized device versions, e.g., sin, and others are only available on certain devices, e.g., __umul64hi. To provide “native” support for math and complex on the respective architecture, Clang will wrap the “native” math functions, e.g., as provided by the device vendor, in an OpenMP begin/end declare variant. These functions will then be picked up instead of the host versions while host only variables and function definitions are still available. Complex arithmetic and functions are support through a similar mechanism. It is worth noting that this support requires extensions to the OpenMP begin/end declare variant context selector that are exposed through LLVM/Clang to the user as well.

Q: What is a way to debug errors from mapping memory to a target device?

An experimental way to debug these errors is to use remote process offloading. By using and openmp-offloading-server, it is possible to explicitly perform memory transfers between processes on the host CPU and run sanitizers while doing so in order to catch these errors.

Q: Why does my application say “Named symbol not found” and abort when I run it?

This is most likely caused by trying to use OpenMP offloading with static libraries. Static libraries do not contain any device code, so when the runtime attempts to execute the target region it will not be found and you will get an an error like this.

CUDA error: Loading '__omp_offloading_fd02_3231c15__Z3foov_l2' Failed
CUDA error: named symbol not found
Libomptarget error: Unable to generate entries table for device id 0.

Currently, the only solution is to change how the application is built and avoid the use of static libraries.

Q: Can I use dynamically linked libraries with OpenMP offloading

Dynamically linked libraries can be only used if there is no device code split between the library and application. Anything declared on the device inside the shared library will not be visible to the application when it’s linked.