Massively Parallel Graphical Model Structure Learning Library

HP-CONCORD (High-Performance CONCORD) is a highly-scalable distributed-memory implementation of the CONCORD-ISTA algorithm for sparse inverse covariance matrix estimation. It is implemented in C++ with MPI and OpenMP. The main bottleneck of HP-CONCORD is iterative sparse-dense matrix-matrix multiplication which is handled by the SpDM3 library.


The code is available on BitBucket. Please cite our papers in the publication section if you use the library.
HP-CONCORD is also available as a pre-compiled NERSC module on the Edison supercomputer. For more details, please refer to the instructions in our BitBucket page.




penpornk (at) eecs (dot) berkeley (dot) edu

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