Par4All is an automatic parallelizing and optimizing compiler (workbench)
for C and Fortran sequential programs.
The purpose of this source-to-source compiler is to adapt existing
applications to various hardware targets such as multicore systems, high
performance computers and GPUs. It creates a new source code and thus
allows the original source code of the application to remain unchanged.
The main benefits of this source-to-source approach are:
- to keep the original source code of the application free from
- to obtain generated parallelized sources for various hardware platforms,
- to rely on vendor’s optimized target tools,
- to be able to optimize manually the generated source code.
The generated sources just need to be processed through the usual compilers:
- optimized compilers for a given processor,
- vendor compilers for embedded processors,
- linkable with MPI and other libraries.
Par4All current version
The current 1.*x* version can generate CUDA and OpenCL code from C code
and OpenMP from C and Fortran 77 code with a simple easy-to-use high-level
script p4a. With this script, you can get a parallelized version of
your sources or even call the backend compiler to generate executable
binaries with gcc, nvcc or icc for example.
On the benchmarks page, there are some performance results
with Par4All on multicores and GPU.
Currently there is no support for Windows. Mac OS X may work by compiling
from the sources but is not supported. But you can use a virtual machine
with Ubuntu or Debian 64-bit x86 on these systems to generate parallel
versions of your programs.
What is going on?
The main development of the 1.4 branch is almost stopped since we are
focusing our developments on the 2.0 version based on Clang that takes
most of our time.
Warning: since the project is no longer supported by SILKAN, most of
the developments are frozen, such as the Clang/LLVM/SoSlang for 2.x
- Switching to Clang as the base framework for Par4All 2.*x*
- Scilab/Xcos and MATLAB/Simulink to OpenMP/CUDA/OpenCL with Wild Cruncher
- Python compilation & parallelization to OpenMP/CUDA/OpenCL
- Code generation for more embedded systems (Tilera, Kalray MPPA, ST
- More user-friendly interfaces (Eclipse...)
- Improving vector code generation (x86 SSE & AVX, ARM Neon, CUDA and
- Better CUDA and OpenCL generation (loop fusion, shared memory...)
- Improving the OpenMP output
- Automatic instrumentation for loop parameters extraction at runtime
- Java compilation & parallelization to OpenMP/CUDA/OpenCL
- Finish the Fortran 95 support with the gcc/gfc front-end
- Par4All 0.1 and 0.2 went out to provide Fortran 77 to OpenMP
parallelization to modernize legacy code and C to OpenMP
parallelization. There were first releases to test the integration
process and were not really distributed as packages or with high level
- Par4All 1.0 (07/2010) parallelizes Fortran and C to OpenMP and C to CUDA
and is the first easy-to-use public version;
- Par4All 1.1 (03/2011) deals with C99 and introduces basic support for
Fortran 95 to OpenMP;
- Par4All 1.2 (07/2011) loop-fusion and communication optimizations for
- Par4All 1.3.1 (01/2012) generates OpenCL;
- Par4All 1.4.3 (09/2012) deal with Spear-DE output;
- Par4All 2.0 : new version based on Clang/LLVM. The developments are on
Internally, Par4All is currently composed of different components:
Par4All is an open source project that merges various open source
developments. More info on the community here.