Achieving Speedups for Distributed Graph Biconnectivity

As data scales continue to increase, studying the porting and implementation of shared memory parallel algorithms for distributed memory architectures becomes increasingly important. We consider the problem of biconnectivity for this current study, which identifies cut vertices and cut edges in a graph. As part of our study, we implemented and optimized a shared memory biconnectivity algorithm based on color propagation within a distributed memory context. This algorithm is neither work nor time efficient. However, when we compare to distributed implementations of theoretically efficient algorithms, we find that simple non-optimal algorithms can greatly outperform time-efficient algorithms in practice when implemented for real distributed-memory environments and real data. Overall, our distributed implementation for computing graph biconnectivity demonstrates an average strong scaling speedup of 15 x across 64 MPI ranks on a suite of irregular real-world inputs. We also note an average of llx and 7.3x speedup relative to the optimal serial algorithm and fastest shared-memory implementation for the biconnectivity problem, respectively.