Towards Fast Overlapping Community Detection

Accelerating sequential algorithms in order to achieve high performance is often a nontrivial task. However, there are certain properties that can exacerbate this process and make it particularly daunting. For example, building an efficient parallel solution for a data-intensive algorithm requires a deep analysis of the memory access patterns and data reuse potential. Attempting to scale out the computations on clusters of machines introduces further complications due to network speed limitations. In this context, the optimization landscape can be extremely complex owing to the large number of trade-off decisions. In this paper, we discuss our experience designing two parallel implementations of an existing data-intensive machine learning algorithm that detects overlapping communities in graphs. The first design uses a single GPU to accelerate the computations of small data sets. We employed a code generation strategy in order to test and identify the best performing combination of optimizations. The second design uses a cluster of machines to scale out the computations for larger problem sizes. We used a mixture of MPI, RDMA and pipelining in order to circumvent networking overhead. Both these efforts bring us closer to understanding the complex relationships hidden within networks of entities