08664d005c
One of the key algorithms used in the "mlir::verify(op)" method is the
dominance checker, which ensures that operand values properly dominate
the operations that use them.
The MLIR dominance implementation has a number of algorithmic problems,
and is not really set up in general to answer dense queries: it's constant
factors are really slow with multiple map lookups and scans, even in the
easy cases. Furthermore, when calling mlir::verify(module) or some other
high level operation, it makes sense to parallelize the dominator
verification of all the functions within the module.
This patch has a few changes to enact this:
1) It splits dominance checking into "IsolatedFromAbove" units. Instead
of building a monolithic DominanceInfo for everything in a module,
for example, it checks dominance for the module to all the functions
within it (noop, since there are no operands at this level) then each
function gets their own DominanceInfo for each of their scope.
2) It adds the ability for mlir::DominanceInfo (and post dom) to be
constrained to an IsolatedFromAbove region. There is no reason to
recurse into IsolatedFromAbove regions since use/def relationships
can't span this region anyway. This is already checked by the time
the verifier gets here.
3) It avoids querying DominanceInfo for trivial checks (e.g. intra Block
references) to eliminate constant factor issues).
4) It switches to lazily constructing DominanceInfo because the trivial
check case handles the vast majority of the cases and avoids
constructing DominanceInfo entirely in some cases (e.g. at the module
level or for many Regions's that contain a single Block).
5) It parallelizes analysis of collections IsolatedFromAbove operations,
e.g. each of the functions within a Module.
All together this is more than a 10% speedup on `firtool` in circt on a
large design when run in -verify-each mode (our default) since the verifier
is invoked after each pass.
Still todo is to parallelize the main verifier pass. I decided to split
this out to its own thing since this patch is already large-ish.
Differential Revision: https://reviews.llvm.org/D103373
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:
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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.