FREDDO is a software runtime, implemented in C++11, that provides data-driven concurrency on single-node and distributed multi-core systems. It is based on Data-Driven Multithreading [1-2], a hybrid control-flow/data-flow model that schedules threads based on data availability on sequential processors.
In case you have any publications resulting from FREDDO, please cite the following publications:
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G. Matheou and P. Evripidou, "FREDDO: an efficient framework for runtime execution of data-driven objects," Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA), 2016. ISBN: 1-60132-444-8. [Online]. Available: http://worldcomp-proceedings.com/proc/p2016/PDP3458.pdf
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G. Matheou and P. Evripidou, "Data-Driven Concurrency for High Performance Computing," ACM Transactions on Architecture and Code Optimization (TACO) 14.4 (2017): 53. DOI: 10.1145/3162014
- GCC compiler with C++11 support
- MPI library with multi-threading support. FREDDO has been tested with OpenMPI (Versions: 1.8.4, 2.0.1 and 2.1.1). However, we expect that FREDDO will work with any MPI version that supports multithreading.
- Go to freddo directory
make clean(will clean the freddo project in the current directory)- make MPI_BIN_PATH=<Path to the MPI's bin directory>
- e.g.,
make MPI_BIN_PATH=/opt/openmpi3/bin/
- e.g.,
make install- FREDDO library will be installed in /usr/local by default. If you want to change the installation directory you should set the FREDDO_INSTALL_DIR variable. For example:
make install FREDDO_INSTALL_DIR=../install_freddo
- FREDDO library will be installed in /usr/local by default. If you want to change the installation directory you should set the FREDDO_INSTALL_DIR variable. For example:
- Go to freddo directory
make uninstall(will remove the binaries/libraries and header files from the installation directory)- If during installation you set a different installation directory than the default (i.e., /usr/local), you should set the FREDDO_INSTALL_DIR variable. For example:
make uninstall FREDDO_INSTALL_DIR=../install_freddo
- If during installation you set a different installation directory than the default (i.e., /usr/local), you should set the FREDDO_INSTALL_DIR variable. For example:
Install ScaLAPACK, LAPACK and PLASMA libraries following the instructions found in docs directory. The SCALAPACK_LIB_DIR, LAPACK_DIR and PLASMA_DIR should be specified, either by modifying the Makefile files or by using them directly along with the make commands, or by exporting them to the shell.
- Go to ./benchmarks directory
make clean- make MPI_BIN_PATH=<Path to the MPI's bin directory> (all benchmarks will be compiled and installed in bin directory)
- e.g.:
make MPI_BIN_PATH=/opt/openmpi3/bin/
- e.g.:
- make <X> MPI_BIN_PATH=<MPI bin directory> (will compile and install the X benchmark)
- e.g.:
make fibonacci MPI_BIN_PATH=/opt/openmpi3/bin/
- e.g.:
- Notice: if during FREDDO installation you set a different installation directory than the default (i.e., /usr/local), you should set the FREDDO_INSTALL_DIR variable, using absolute paths. For example:
- e.g.:
make MPI_BIN_PATH=/opt/openmpi3/bin/ FREDDO_INSTALL_DIR=~/Desktop/Workspace/freddo-project/install_freddo/ - e.g.:
make fibonacci MPI_BIN_PATH=/opt/openmpi3/bin/ FREDDO_INSTALL_DIR=~/Desktop/Workspace/freddo-project/install_freddo/
- e.g.:
- Execute a benchmark as an MPI application. Here we show how we run Cholesky using OpenMPI (V3).
- Run Cholesky on the same machine using 2 MPI processes (not recommended, only for testing)
/opt/openmpi3/bin/mpirun -np 2 --npernode 2 ./bin/cholesky/cholesky_scalapack_with_coll 2 1024 32 1
- Run Chokesky on a 4-node cluster (one MPI process per node)
/opt/openmpi3/bin/mpirun -np 4 --machinefile peers.txt --npernode 1 ./bin/cholesky/cholesky_scalapack_with_coll 2 1024 32 1
- Run Cholesky on the same machine using 2 MPI processes (not recommended, only for testing)
[1] Kyriacou, Costas, Paraskevas Evripidou, and Pedro Trancoso. "Data-driven multithreading using conventional microprocessors." IEEE Transactions on Parallel and Distributed Systems 17.10 (2006): 1176-1188.
[2] Matheou, George, and Paraskevas Evripidou. "Architectural support for data-driven execution." ACM Transactions on Architecture and Code Optimization (TACO) 11.4 (2015): 52.