forked from OSchip/llvm-project
132 lines
4.8 KiB
ReStructuredText
132 lines
4.8 KiB
ReStructuredText
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.. contents::
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:local:
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==================
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OpenMP Support
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==================
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Clang supports the following OpenMP 5.0 features
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* The `reduction`-based clauses in the `task` and `target`-based directives.
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* Support relational-op != (not-equal) as one of the canonical forms of random
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access iterator.
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* Support for mapping of the lambdas in target regions.
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* Parsing/sema analysis for the requires directive.
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* Nested declare target directives.
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* Make the `this` pointer implicitly mapped as `map(this[:1])`.
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* The `close` *map-type-modifier*.
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Clang fully supports OpenMP 4.5. Clang supports offloading to X86_64, AArch64,
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PPC64[LE] and has `basic support for Cuda devices`_.
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* #pragma omp declare simd: :partial:`Partial`. We support parsing/semantic
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analysis + generation of special attributes for X86 target, but still
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missing the LLVM pass for vectorization.
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In addition, the LLVM OpenMP runtime `libomp` supports the OpenMP Tools
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Interface (OMPT) on x86, x86_64, AArch64, and PPC64 on Linux, Windows, and macOS.
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General improvements
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--------------------
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- New collapse clause scheme to avoid expensive remainder operations.
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Compute loop index variables after collapsing a loop nest via the
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collapse clause by replacing the expensive remainder operation with
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multiplications and additions.
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- The default schedules for the `distribute` and `for` constructs in a
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parallel region and in SPMD mode have changed to ensure coalesced
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accesses. For the `distribute` construct, a static schedule is used
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with a chunk size equal to the number of threads per team (default
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value of threads or as specified by the `thread_limit` clause if
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present). For the `for` construct, the schedule is static with chunk
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size of one.
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- Simplified SPMD code generation for `distribute parallel for` when
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the new default schedules are applicable.
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.. _basic support for Cuda devices:
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Cuda devices support
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====================
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Directives execution modes
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--------------------------
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Clang code generation for target regions supports two modes: the SPMD and
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non-SPMD modes. Clang chooses one of these two modes automatically based on the
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way directives and clauses on those directives are used. The SPMD mode uses a
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simplified set of runtime functions thus increasing performance at the cost of
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supporting some OpenMP features. The non-SPMD mode is the most generic mode and
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supports all currently available OpenMP features. The compiler will always
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attempt to use the SPMD mode wherever possible. SPMD mode will not be used if:
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- The target region contains an `if()` clause that refers to a `parallel`
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directive.
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- The target region contains a `parallel` directive with a `num_threads()`
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clause.
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- The target region contains user code (other than OpenMP-specific
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directives) in between the `target` and the `parallel` directives.
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Data-sharing modes
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------------------
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Clang supports two data-sharing models for Cuda devices: `Generic` and `Cuda`
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modes. The default mode is `Generic`. `Cuda` mode can give an additional
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performance and can be activated using the `-fopenmp-cuda-mode` flag. In
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`Generic` mode all local variables that can be shared in the parallel regions
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are stored in the global memory. In `Cuda` mode local variables are not shared
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between the threads and it is user responsibility to share the required data
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between the threads in the parallel regions.
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Collapsed loop nest counter
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---------------------------
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When using the collapse clause on a loop nest the default behavior is to
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automatically extend the representation of the loop counter to 64 bits for
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the cases where the sizes of the collapsed loops are not known at compile
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time. To prevent this conservative choice and use at most 32 bits,
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compile your program with the `-fopenmp-optimistic-collapse`.
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Features not supported or with limited support for Cuda devices
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---------------------------------------------------------------
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- Cancellation constructs are not supported.
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- Doacross loop nest is not supported.
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- User-defined reductions are supported only for trivial types.
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- Nested parallelism: inner parallel regions are executed sequentially.
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- Static linking of libraries containing device code is not supported yet.
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- Automatic translation of math functions in target regions to device-specific
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math functions is not implemented yet.
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- Debug information for OpenMP target regions is supported, but sometimes it may
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be required to manually specify the address class of the inspected variables.
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In some cases the local variables are actually allocated in the global memory,
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but the debug info may be not aware of it.
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