Collaborative (CPU + GPU) algorithms for triangle counting and truss decomposition on the Minsky architecture: Static graph challenge: Subgraph isomorphism

Abstract

In this paper, we present collaborative CPU + GPU algorithms for triangle counting and truss decomposition, the two fundamental problems in graph analytics. We describe the implementation details and present experimental evaluation on the IBM Minsky platform. The main contribution of this paper is a thorough benchmarking and comparison of the different memory management schemes offered by CUDA 8 and NVLink, which can be harnessed for tackling large problems where the limited GPU memory capacity is the primary bottleneck in traditional computing platform. We find that the collaborative algorithms achieve 28× speedup on average (180× max) for triangle counting, and 165× speedup on average (498× max) for truss decomposition, when compared with the baseline Python implementation provided by the Graph Challenge organizers.