  Massively parallel architectures such as the GPU are becoming increasingly
important due to the recent proliferation of data. In this paper, we propose a
key class of hybrid parallel graphlet algorithms that leverages multiple CPUs
and GPUs simultaneously for computing k-vertex induced subgraph statistics
(called graphlets). In addition to the hybrid multi-core CPU-GPU framework, we
also investigate single GPU methods (using multiple cores) and multi-GPU
methods that leverage all available GPUs simultaneously for computing induced
subgraph statistics. Both methods leverage GPU devices only, whereas the hybrid
multi-core CPU-GPU framework leverages all available multi-core CPUs and
multiple GPUs for computing graphlets in large networks. Compared to recent
approaches, our methods are orders of magnitude faster, while also more cost
effective enjoying superior performance per capita and per watt. In particular,
the methods are up to 300 times faster than the recent state-of-the-art method.
To the best of our knowledge, this is the first work to leverage multiple CPUs
and GPUs simultaneously for computing induced subgraph statistics.
