# Clang 8 documentation

## OpenMP Support

- OpenMP Support
  - General improvements
    - Cuda devices support
  - Directives execution modes
  - Data-sharing modes
  - Collapsed loop nest counter
  - Features not supported or with limited support for Cuda devices

# OpenMP Support

Clang supports the following OpenMP 5.0 features

- The reduction-based clauses in the task and target-based directives.
- Support relational-op != (not-equal) as one of the canonical forms of random access iterator.
- Support for mapping of the lambdas in target regions.
- Parsing/sema analysis for the requires directive.
- Nested declare target directives.
- Make the this pointer implicitly mapped as map(this[:1]).
- The close map-type-modifier.

Clang fully supports OpenMP 4.5. Clang supports offloading to X86_64, AArch64, PPC64[LE] and has basic support for Cuda devices.

- #pragma omp declare simd: Partial. We support parsing/semantic analysis + generation of special attributes for X86 target, but still missing the LLVM pass for vectorization.

In addition, the LLVM OpenMP runtime libomp supports the OpenMP Tools Interface (OMPT) on x86, x86_64, AArch64, and PPC64 on Linux, Windows, and macOS.

## General improvements

- New collapse clause scheme to avoid expensive remainder operations. Compute loop index variables after collapsing a loop nest via the collapse clause by replacing the expensive remainder operation with multiplications and additions.
- The default schedules for the distribute and for constructs in a parallel region and in SPMD mode have changed to ensure coalesced accesses. For the distribute construct, a static schedule is used with a chunk size equal to the number of threads per team (default value of threads or as specified by the thread_limit clause if present). For the for construct, the schedule is static with chunk size of one.
- Simplified SPMD code generation for distribute parallel for when the new default schedules are applicable.

### Cuda devices support

## Directives execution modes

Clang code generation for target regions supports two modes: the SPMD and non-SPMD modes. Clang chooses one of these two modes automatically based on the way directives and clauses on those directives are used. The SPMD mode uses a simplified set of runtime functions thus increasing performance at the cost of supporting some OpenMP features. The non-SPMD mode is the most generic mode and supports all currently available OpenMP features. The compiler will always attempt to use the SPMD mode wherever possible. SPMD mode will not be used if:

> - The target region contains an if() clause that refers to a parallel directive.
> - The target region contains a parallel directive with a num_threads() clause.
> - The target region contains user code (other than OpenMP-specific directives) in between the target and the parallel directives.

## Data-sharing modes

Clang supports two data-sharing models for Cuda devices: Generic and Cuda modes. The default mode is Generic. Cuda mode can give an additional performance and can be activated using the -fopenmp-cuda-mode flag. In Generic mode all local variables that can be shared in the parallel regions are stored in the global memory. In Cuda mode local variables are not shared between the threads and it is user responsibility to share the required data between the threads in the parallel regions.

## Collapsed loop nest counter

When using the collapse clause on a loop nest the default behaviour is to automatically extend the representation of the loop counter to 64 bits for the cases where the sizes of the collapsed loops are not known at compile time. To prevent this conservative choice and use at most 32 bits, compile your program with the -fopenmp-optimistic-collapse.

## Features not supported or with limited support for Cuda devices

- Cancellation constructs are not supported.
- Doacross loop nest is not supported.
- User-defined reductions are supported only for trivial types.
- Nested parallelism: inner parallel regions are executed sequentially.
- Static linking of libraries containing device code is not supported yet.
- Automatic translation of math functions in target regions to device-specific math functions is not implemented yet.
- Debug information for OpenMP target regions is not supported yet.