[NFC][Py Reformat] Reformat python files in the rest of the dirs

This is an ongoing series of commits that are reformatting our
Python code. This catches the last of the python files to
reformat. Since they where so few I bunched them together.

Reformatting is done with `black`.

If you end up having problems merging this commit because you
have made changes to a python file, the best way to handle that
is to run git checkout --ours <yourfile> and then reformat it
with black.

If you run into any problems, post to discourse about it and
we will try to help.

RFC Thread below:

https://discourse.llvm.org/t/rfc-document-and-standardize-python-code-style

Reviewed By: jhenderson, #libc, Mordante, sivachandra

Differential Revision: https://reviews.llvm.org/D150784

GitOrigin-RevId: f98ee40f4b5d7474fc67e82824bf6abbaedb7b1c
1 file changed
tree: 63787e64d6d2448f8ab0d466681bae8dc6ccd095
  1. cmake/
  2. docs/
  3. include/
  4. test/
  5. .clang-format
  6. CMakeLists.txt
  7. CREDITS.txt
  8. LICENSE.TXT
  9. README.md
README.md

Parallel STL

Parallel STL is an implementation of the C++ standard library algorithms with support for execution policies, as specified in ISO/IEC 14882:2017 standard, commonly called C++17. The implementation also supports the unsequenced execution policy specified in Parallelism TS version 2 and proposed for the next version of the C++ standard in the C++ working group paper P1001. Parallel STL offers efficient support for both parallel and vectorized execution of algorithms. For sequential execution, it relies on an available implementation of the C++ standard library.

Prerequisites

To use Parallel STL, you must have the following software installed:

  • C++ compiler with:
    • Support for C++11
    • Support for OpenMP* 4.0 SIMD constructs
  • Threading Building Blocks (TBB) which is available for download at https://github.com/01org/tbb/

Known issues and limitations

  • unseq and par_unseq policies only have effect with compilers that support #pragma omp simd or #pragma simd.
  • Parallel and vector execution is only supported for the algorithms if random access iterators are provided, while for other iterator types the execution will remain serial.
  • The following algorithms do not allow efficient SIMD execution: includes, inplace_merge, merge, nth_element, partial_sort, partial_sort_copy, set_difference, set_intersection, set_symmetric_difference, set_union, sort, stable_partition, stable_sort, unique.
  • The initial value type for exclusive_scan, inclusive_scan, transform_exclusive_scan, transform_inclusive_scan shall be DefaultConstructible. A default constructed-instance of the initial value type shall be the identity element for the specified binary operation.
  • For max_element, min_element, minmax_element, partial_sort, partial_sort_copy, sort, stable_sort the dereferenced value type of the provided iterators shall be DefaultConstructible.
  • For remove, remove_if, unique the dereferenced value type of the provided iterators shall be MoveConstructible.
  • The following algorithms require additional O(n) memory space for parallel execution: copy_if, inplace_merge, partial_sort, partial_sort_copy, partition_copy, remove, remove_if, rotate, sort, stable_sort, unique, unique_copy.