Communication-aware Mapping of Stream Graphs for Multi-GPU Platforms

Abstract

Stream graphs can provide a natural way to represent many applications in multimedia and DSP domains. Though the exposed parallelism of stream graphs makes it relatively easy to map them to GPGPUs, very large stream graphs as well as how to best exploit multi-GPU platforms to achieve scalable performance poses great challenges for stream graph mapping. Previous work considers either a single GPU only or is based on a crude heuristic that achieves a very low degree of workload balancing, and thus shows only limited scalability. In this paper we present a highly scalable GP-GPU mapping technique for large stream graphs with the following highlights: (1) an accurate GPU performance estimation model for (subsets of) stream graphs, (2) a novel partitioning heuristic exploiting stream graph’s structural properties, and (3) ILP (Integer Linear Programming) formulation of the mapping problem. Our experimental results on a real GPU platform demonstrate that our techniques can generate significantly better performance than the current state of the art, in both single GPU and multi-GPU cases.