Hybrid CPU-GPU Framework for Network Motifs

TL;DR

This paper introduces a hybrid CPU-GPU framework that significantly accelerates the computation of network motifs, outperforming existing methods by up to 300 times and efficiently utilizing multiple processing units.

Contribution

It presents the first hybrid multi-core CPU-GPU approach for network motif computation, achieving substantial speedups and improved cost efficiency over prior methods.

Findings

Methods are up to 300 times faster than recent approaches.

Hybrid CPU-GPU framework leverages all available hardware for large networks.

Achieves better performance per watt and per capita.

Abstract

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.