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chromium / external / github.com / llvm / llvm-project.git / da25f968a90ad4560fc920a6d18fc2a0221d2750
commitda25f968a90ad4560fc920a6d18fc2a0221d2750[log] [tgz]
authorPeter Klausler <[email protected]>Tue Nov 02 20:01:38 2021
committerPeter Klausler <[email protected]>Fri Nov 12 19:40:02 2021
treeccf0e57ffafbdd02bc32439f961bada36fdf17ed
parenteb6f9f3123e6043d559fdfd921714626ff542cee [diff]
[flang] Runtime performance improvements to real formatted input

Profiling a basic internal real input read benchmark shows some
hot spots in the code used to prepare input for decimal-to-binary
conversion, which is of course where the time should be spent.
The library that implements decimal to/from binary conversions has
been optimized, but not the code in the Fortran runtime that calls it,
and there are some obvious light changes worth making here.

Move some member functions from *.cpp files into the class definitions
of Descriptor and IoStatementState to enable inlining and specialization.

Make GetNextInputBytes() the new basic input API within the
runtime, replacing GetCurrentChar() -- which is rewritten in terms of
GetNextInputBytes -- so that input routines can have the
ability to acquire more than one input character at a time
and amortize overhead.

These changes speed up the time to read 1M random reals
using internal I/O from a character array from 1.29s to 0.54s
on my machine, which on par with Intel Fortran and much faster than
GNU Fortran.

Differential Revision: https://reviews.llvm.org/D113697
13 files changed
tree: ccf0e57ffafbdd02bc32439f961bada36fdf17ed
  1. .github/
  2. clang/
  3. clang-tools-extra/
  4. cmake/
  5. compiler-rt/
  6. cross-project-tests/
  7. flang/
  8. libc/
  9. libclc/
  10. libcxx/
  11. libcxxabi/
  12. libunwind/
  13. lld/
  14. lldb/
  15. llvm/
  16. mlir/
  17. openmp/
  18. polly/
  19. pstl/
  20. runtimes/
  21. utils/
  22. .arcconfig
  23. .arclint
  24. .clang-format
  25. .clang-tidy
  26. .git-blame-ignore-revs
  27. .gitignore
  28. .mailmap
  29. CONTRIBUTING.md
  30. README.md
  31. SECURITY.md
README.md

The LLVM Compiler Infrastructure

This directory and its sub-directories contain source code for LLVM, a toolkit for the construction of highly optimized compilers, optimizers, and run-time environments.

The README briefly describes how to get started with building LLVM. For more information on how to contribute to the LLVM project, please take a look at the Contributing to LLVM guide.

Getting Started with the LLVM System

Taken from https://llvm.org/docs/GettingStarted.html.

Overview

Welcome to the LLVM project!

The LLVM project has multiple components. The core of the project is itself called “LLVM”. This contains all of the tools, libraries, and header files needed to process intermediate representations and convert them into object files. Tools include an assembler, disassembler, bitcode analyzer, and bitcode optimizer. It also contains basic regression tests.

C-like languages use the Clang front end. This component compiles C, C++, Objective-C, and Objective-C++ code into LLVM bitcode -- and from there into object files, using LLVM.

Other components include: the libc++ C++ standard library, the LLD linker, and more.

Getting the Source Code and Building LLVM

The LLVM Getting Started documentation may be out of date. The Clang Getting Started page might have more accurate information.

This is an example work-flow and configuration to get and build the LLVM source:

  1. Checkout LLVM (including related sub-projects like Clang):

    • git clone https://github.com/llvm/llvm-project.git

    • Or, on windows, git clone --config core.autocrlf=false https://github.com/llvm/llvm-project.git

  2. Configure and build LLVM and Clang:

    • cd llvm-project

    • cmake -S llvm -B build -G <generator> [options]

      Some common build system generators are:

      • Ninja --- for generating Ninja build files. Most llvm developers use Ninja.
      • Unix Makefiles --- for generating make-compatible parallel makefiles.
      • Visual Studio --- for generating Visual Studio projects and solutions.
      • Xcode --- for generating Xcode projects.

      Some common options:

      • -DLLVM_ENABLE_PROJECTS='...' --- semicolon-separated list of the LLVM sub-projects you'd like to additionally build. Can include any of: clang, clang-tools-extra, compiler-rt,cross-project-tests, flang, libc, libclc, libcxx, libcxxabi, libunwind, lld, lldb, mlir, openmp, polly, or pstl.

        For example, to build LLVM, Clang, libcxx, and libcxxabi, use -DLLVM_ENABLE_PROJECTS="clang;libcxx;libcxxabi".

      • -DCMAKE_INSTALL_PREFIX=directory --- Specify for directory the full path name of where you want the LLVM tools and libraries to be installed (default /usr/local).

      • -DCMAKE_BUILD_TYPE=type --- Valid options for type are Debug, Release, RelWithDebInfo, and MinSizeRel. Default is Debug.

      • -DLLVM_ENABLE_ASSERTIONS=On --- Compile with assertion checks enabled (default is Yes for Debug builds, No for all other build types).

    • cmake --build build [-- [options] <target>] or your build system specified above directly.

      • The default target (i.e. ninja or make) will build all of LLVM.

      • The check-all target (i.e. ninja check-all) will run the regression tests to ensure everything is in working order.

      • CMake will generate targets for each tool and library, and most LLVM sub-projects generate their own check-<project> target.

      • Running a serial build will be slow. To improve speed, try running a parallel build. That's done by default in Ninja; for make, use the option -j NNN, where NNN is the number of parallel jobs, e.g. the number of CPUs you have.

    • For more information see CMake

Consult the Getting Started with LLVM page for detailed information on configuring and compiling LLVM. You can visit Directory Layout to learn about the layout of the source code tree.