9982f33e2c
This patch does the following: 1) Introduce kmp_topology_t as the runtime-friendly structure (the corresponding global variable is __kmp_topology) to determine the exact machine topology which can vary widely among current and future architectures. The current design is not easy to expand beyond the assumed three layer topology: sockets, cores, and threads so a rework capable of using the existing KMP_AFFINITY mechanisms is required. This new topology structure has: * The depth and types of the topology * Ratio count for each consecutive level (e.g., number of cores per socket, number of threads per core) * Absolute count for each level (e.g., 2 sockets, 16 cores, 32 threads) * Equivalent topology layer map (e.g., Numa domain is equivalent to socket, L1/L2 cache equivalent to core) * Whether it is uniform or not The hardware threads are represented with the kmp_hw_thread_t structure. This structure contains the ids (e.g., socket 0, core 1, thread 0) and other information grabbed from the previous Address structure. The kmp_topology_t structure contains an array of these. 2) Generalize the KMP_HW_SUBSET envirable for the new kmp_topology_t structure. The algorithm doesn't assume any order with tiles,numa domains,sockets,cores,threads. Instead it just parses the envirable, makes sure it is consistent with the detected topology (including taking into account equivalent layers) and then trims away the unneeded subset of hardware threads. To enable this, a new kmp_hw_subset_t structure is introduced which contains a vector of items (hardware type, number user wants, offset). Any keyword within __kmp_hw_get_keyword() can be used as a name and can be shortened as well. e.g., KMP_HW_SUBSET=1s,2numa,4tile,2c,3t can be used on the KNL SNC-4 machine. 3) Simplify topology detection functions so they only do the singular task of detecting the machine's topology. Printing, and all canonicalizing functionality is now done afterwards. So many lines of duplicated code are eliminated. 4) Add new ll_caches and numa_domains to OMP_PLACES, and consequently, KMP_AFFINITY's granularity setting. All the names within __kmp_hw_get_keyword() are available for use in OMP_PLACES or KMP_AFFINITY's granularity setting. 5) Simplify and future-proof code where explicit lists of allowed affinity settings keywords inside if() conditions. 6) Add x86 CPUID leaf 4 cache detection to existing x2apic id method so equivalent caches could be detected (in particular for the ll_caches place). Differential Revision: https://reviews.llvm.org/D100997 |
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| .github | ||
| clang | ||
| clang-tools-extra | ||
| compiler-rt | ||
| debuginfo-tests | ||
| flang | ||
| libc | ||
| libclc | ||
| libcxx | ||
| libcxxabi | ||
| libunwind | ||
| lld | ||
| lldb | ||
| llvm | ||
| mlir | ||
| openmp | ||
| parallel-libs | ||
| polly | ||
| pstl | ||
| runtimes | ||
| utils/arcanist | ||
| .arcconfig | ||
| .arclint | ||
| .clang-format | ||
| .clang-tidy | ||
| .git-blame-ignore-revs | ||
| .gitignore | ||
| CONTRIBUTING.md | ||
| README.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 converts it 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:
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Checkout LLVM (including related sub-projects like Clang):
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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
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Configure and build LLVM and Clang:
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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:
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-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, libcxx, libcxxabi, libunwind, lldb, compiler-rt, lld, polly, or debuginfo-tests.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).
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cmake --build build [-- [options] <target>]or your build system specified above directly.-
The default target (i.e.
ninjaormake) will build all of LLVM. -
The
check-alltarget (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, whereNNNis the number of parallel jobs, e.g. the number of CPUs you have.
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For more information see CMake
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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.