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The Combinatorial BLAS (CombBLAS) is an extensible distributed-memory parallel graph library offering a small but powerful set of linear algebra primitives specifically targeting graph analytics.
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PASSIONLab/CombBLAS
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Installation ------------ KDT is distributed as a Python Distutils package. It requires the MPI compiler to be specified in the $CC and $CXX environment variables. For example, in Bash: export CC=mpicxx export CXX=mpicxx The build and installation is performed by the standard Distutils setup command: $ python setup.py build $ sudo python setup.py install System Requirements ------------------- KDT makes use of some features from C++ TR1. If your compiler does not support TR1 then the free Boost C++ library (http://www.boost.org/) supplies the required headers as well. Make sure boost/ is in your include path. If it is not, you can append the include path with the -I switch to the setup.py script. For example, if you installed Boost in /home/username/include/boost: $ python setup.py build -I/home/username/include MPI library must be compiled with -fPIC. Python modules must be compiled with -fPIC, and that includes all libraries that get linked into the module. That includes the MPI library in the case of KDT. If your MPI was not compiled with -fPIC then the link step will fail. If this happens, contact your system administrator.
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The Combinatorial BLAS (CombBLAS) is an extensible distributed-memory parallel graph library offering a small but powerful set of linear algebra primitives specifically targeting graph analytics.
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