Starting with LLVM/10, the Archer runtime is included in LLVM releases. Any further development is directly upstreamed into LLVM (https://github.com/llvm/llvm-project/). For productive use of Archer we suggest to use Archer in LLVM. Report bugs at http://bugs.llvm.org/ under OpenMP/runtime.
Archer is distributed under the terms of the Apache License.
Please see LICENSE for usage terms.
LLNL-CODE-773957
Archer is a data race detector for OpenMP programs.
Archer combines static and dynamic techniques to identify data races in large OpenMP applications, leading to low runtime and memory overheads, while still offering high accuracy and precision. It builds on open-source tools infrastructure such as LLVM, ThreadSanitizer, and OMPT to provide portability.
To compile Archer you need a host Clang/LLVM version >= 3.9, a CMake version >= 3.4.3.
Ninja build system is preferred. For more information how to obtain Ninja visit https://github.com/ninja-build/ninja. (Note that this is different than PRUNERS NINJA tool.)
Archer has been tested with the LLVM OpenMP Runtime version >= 3.9, and with the LLVM OpenMP Runtime with OMPT support currently under development at https://github.com/OpenMPToolsInterface/LLVM-openmp (under the branch "tr4-stable").
Archer has been developed under LLVM 3.9 (for more information visit http://llvm.org).
For an automatic building script (recommended) please visit the GitHub page https://github.com/PRUNERS/llvm_archer.
Archer comes as an LLVM tool, it can be compiled both as a stand-alone tool or within the Clang/LLVM infrastructure.
In order to obtain and build Archer, follow the instructions below for stand-alone or full Clang/LLVM with Archer support (instructions are based on bash shell, Clang/LLVM 3.9 version, Ninja build system, and the LLVM OpenMP Runtime with OMPT support).
Create a folder in which to download and build Archer:
export ARCHER_BUILD=$PWD/ArcherBuild
mkdir $ARCHER_BUILD && cd $ARCHER_BUILD
Obtain the LLVM OpenMP Runtime with OMPT support:
git clone https://github.com/llvm-mirror/openmp.git openmp
and build it with the following command:
export OPENMP_INSTALL=$HOME/usr # or any other install path
cd openmp/runtime
mkdir build && cd build
cmake -G Ninja \
-D CMAKE_C_COMPILER=clang \
-D CMAKE_CXX_COMPILER=clang++ \
-D CMAKE_BUILD_TYPE=Release \
-D CMAKE_INSTALL_PREFIX:PATH=$OPENMP_INSTALL \
-D LIBOMP_OMPT_SUPPORT=on \
-D LIBOMP_OMPT_BLAME=on \
-D LIBOMP_OMPT_TRACE=on \
..
ninja -j8 -l8 # or any number of available cores
ninja install
Obtain Archer:
cd $ARCHER_BUILD
git clone https://github.com/PRUNERS/archer.git archer
and build it with the following commands:
export ARCHER_INSTALL=$HOME/usr # or any other install path
cd archer
mkdir build && cd build
cmake -G Ninja \
-D CMAKE_C_COMPILER=clang \
-D CMAKE_CXX_COMPILER=clang++ \
-D OMP_PREFIX:PATH=$OPENMP_INSTALL \
-D CMAKE_INSTALL_PREFIX:PATH=${ARCHER_INSTALL} \
..
ninja -j8 -l8 # or any number of available cores
ninja install
cd ../..
Create a folder in which to download and build Clang/LLVM and Archer:
export ARCHER_BUILD=$PWD/ArcherBuild
mkdir $ARCHER_BUILD && cd $ARCHER_BUILD
Obtain LLVM:
git clone https://github.com/llvm-mirror/llvm.git llvm_src
cd llvm_src
git checkout release_39
Obtain Clang:
cd tools
git clone https://github.com/llvm-mirror/clang.git clang
cd clang
git checkout release_39
cd ..
Obtain Archer:
cd tools
git clone https://github.com/PRUNERS/archer.git archer
cd ..
Obtain the LLVM compiler-rt:
cd projects
git clone https://github.com/llvm-mirror/compiler-rt.git compiler-rt
cd compiler-rt
git checkout release_39
cd ../..
Obtain LLVM libc++:
cd projects
git clone https://github.com/llvm-mirror/libcxx.git
cd libcxx
git checkout release_39
cd ../..
Obtain LLVM libc++abi:
cd projects
git clone https://github.com/llvm-mirror/libcxxabi.git
cd libcxxabi
git checkout release_39
cd ../..
Obtain LLVM libunwind:
cd projects
git clone https://github.com/llvm-mirror/libunwind.git
cd libunwind
git checkout release_39
cd ../..
Obtain official LLVM OpenMP Runtime:
cd projects
git clone https://github.com/llvm-mirror/openmp.git openmp
Now that we obtained the source code, the following command will build LLVM/Clang infrastructure with Archer support.
First we boostrap clang:
cd $ARCHER_BUILD
mkdir -p llvm_bootstrap
cd llvm_bootstrap
CC=$(which gcc) CXX=$(which g++) cmake -G Ninja \
-DCMAKE_BUILD_TYPE=Release \
-DLLVM_TOOL_ARCHER_BUILD=OFF \
-DLLVM_TARGETS_TO_BUILD=Native \
../llvm_src
ninja -j8 -l8 # or any number of available cores
cd ..
export LD_LIBRARY_PATH="$ARCHER_BUILD/llvm_bootstrap/lib:${LD_LIBRARY_PATH}"
export PATH="$ARCHER_BUILD/llvm_bootstrap/bin:${PATH}"
Then, we can actually build LLVM/Clang with Archer support.
In case of official LLVM OpenMP Runtime run:
export LLVM_INSTALL=$HOME/usr # or any other install path
mkdir llvm_build && cd llvm_build
cmake -G Ninja \
-D CMAKE_C_COMPILER=clang \
-D CMAKE_CXX_COMPILER=clang++ \
-D CMAKE_BUILD_TYPE=Release \
-D OMP_PREFIX:PATH=$LLVM_INSTALL \
-D CMAKE_INSTALL_PREFIX:PATH=$LLVM_INSTALL \
-D CLANG_DEFAULT_OPENMP_RUNTIME:STRING=libomp \
-D LLVM_ENABLE_LIBCXX=ON \
-D LLVM_ENABLE_LIBCXXABI=ON \
-D LIBCXXABI_USE_LLVM_UNWINDER=ON \
-D CLANG_DEFAULT_CXX_STDLIB=libc++ \
-D LIBOMP_TSAN_SUPPORT=TRUE \
../llvm_src
ninja -j8 -l8 # or any number of available cores
ninja check-libarcher
ninja install
Otherwise, in case of LLVM OpenMP Runtime with OMPT support run:
export LLVM_INSTALL=$HOME/usr # or any other install path
mkdir llvm_build && cd llvm_build
cmake -G Ninja \
-D CMAKE_C_COMPILER=clang \
-D CMAKE_CXX_COMPILER=clang++ \
-D CMAKE_BUILD_TYPE=Release \
-D OMP_PREFIX:PATH=$LLVM_INSTALL \
-D CMAKE_INSTALL_PREFIX:PATH=$LLVM_INSTALL \
-D CLANG_DEFAULT_OPENMP_RUNTIME:STRING=libomp \
-D LLVM_ENABLE_LIBCXX=ON \
-D LLVM_ENABLE_LIBCXXABI=ON \
-D LIBCXXABI_USE_LLVM_UNWINDER=ON \
-D CLANG_DEFAULT_CXX_STDLIB=libc++ \
-D LIBOMP_OMPT_SUPPORT=on \
-D LIBOMP_OMPT_BLAME=on \
-D LIBOMP_OMPT_TRACE=on \
../llvm_src
ninja -j8 -l8 # or any number of available cores
ninja check-libarcher
ninja install
Once the installation completes, you need to setup your environment to allow Archer to work correctly.
Please set the following path variables:
export PATH=${LLVM_INSTALL}/bin:${PATH}"
export LD_LIBRARY_PATH=${LLVM_INSTALL}/lib:${LD_LIBRARY_PATH}"
To make the environment permanent, add the previous lines or equivalents to your shell start-up script such as "~/.bashrc".
Archer provides a command to compile your programs with Clang/LLVM OpenMP and hide all the mechanisms necessary to detect data races automatically in your OpenMP programs.
The Archer compile command is called clang-archer, and this can be used as a drop-in replacement of your compiler command (e.g., clang, gcc, etc.).
The following are some of the examples of how one can integrate clang-archer into his/her build system.
If you are using Archer and the LLVM OpenMP Runtime with OMPT support, it is necessary to link your executable against the Archer runtime library libarcher.so. (In the example below the runtime library will be shown in square brackets).
clang-archer example.c -o example [ -L/path/to/archer/runtime/library -larcher ]
In your Makefile, set the following variables:
CC=clang-archer
[ LD_FLAGS=-L/path/to/archer/runtime/library -larcher ]
In your Makefile, set the following variables:
CC = mpicc -cc=clang-archer
[ LD_FLAGS=-L/path/to/archer/runtime/library -larcher ]
The command clang-archer works as a compiler wrapper, all the options available for clang are also available for clang-archer.
| Flag Name | Default value | Clang/LLVM Version | Description |
|---|---|---|---|
| --sa | disabled | >= 6.0.1 | Enable static analysis (can reduce runtime and memory overhead). |
Runtime flags are passed via ARCHER_OPTIONS environment variable, different flags are separated by spaces, e.g.:
ARCHER_OPTIONS="flush_shadow=1" ./myprogram
| Flag Name | Default value | Clang/LLVM Version | Description |
|---|---|---|---|
| flush_shadow | 0 | >= 4.0 | Flush shadow memory at the end of an outer OpenMP parallel region. Our experiments show that this can reduce memory overhead by ~30% and runtime overhead by ~10%. This flag is useful for large OpenMP applications that typically require large amounts of memory, causing out-of-memory exceptions when checked by Archer. |
| print_ompt_counters | 0 | >= 3.9 | Print the number of triggered OMPT events at the end of the execution. |
| print_max_rss | 0 | >= 3.9 | Print the RSS memory peak at the end of the execution. |
Let us take the program below and follow the steps to compile and check the program for data races.
Suppose our program is called myprogram.c:
1 #include <stdio.h>
2
3 #define N 1000
4
5 int main (int argc, char **argv)
6 {
7 int a[N];
8
9 #pragma omp parallel for
10 for (int i = 0; i < N - 1; i++) {
11 a[i] = a[i + 1];
12 }
13 }
In case we installed Archer with the official LLVM OpenMP runtime and ThreadSanitizer support, we compile the program as follow:
clang-archer myprogram.c -o myprogram
otherwise, if we installed Archer with the LLVM OpenMP runtime and ThreadSanitizer OMPT support our compile command will look like:
clang-archer myprogram.c -o myprogram -larcher
Now we can run the program with the following commands:
export OMP_NUM_THREADS=2
./myprogram
Archer will output a report in case it finds data races. In our case the report will look as follow:
==================
WARNING: ThreadSanitizer: data race (pid=13641)
Read of size 4 at 0x7fff79a01170 by main thread:
#0 .omp_outlined. myprogram.c:11:12 (myprogram+0x00000049b5a2)
#1 __kmp_invoke_microtask <null> (libomp.so+0x000000077842)
#2 __libc_start_main /build/glibc-t3gR2i/glibc-2.23/csu/../csu/libc-start.c:291 (libc.so.6+0x00000002082f)
Previous write of size 4 at 0x7fff79a01170 by thread T1:
#0 .omp_outlined. myprogram.c:11:10 (myprogram+0x00000049b5d6)
#1 __kmp_invoke_microtask <null> (libomp.so+0x000000077842)
Location is stack of main thread.
Thread T1 (tid=13643, running) created by main thread at:
#0 pthread_create tsan_interceptors.cc:902:3 (myprogram+0x00000043db75)
#1 __kmp_create_worker <null> (libomp.so+0x00000006c364)
#2 __libc_start_main /build/glibc-t3gR2i/glibc-2.23/csu/../csu/libc-start.c:291 (libc.so.6+0x00000002082f)
SUMMARY: ThreadSanitizer: data race myprogram.c:11:12 in .omp_outlined.
==================
ThreadSanitizer: reported 1 warnings
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