The impact of heterogeneous multi-core clusters on graph partitioning: an empirical study

The advent of multi-core architectures provides an opportunity for accelerating parallelism in mesh-based applications. This multi-core environment, however, imposes challenges not addressed by conventional graph-partitioning techniques that are originally designed for distributed-memory uniprocessors. As the first step to exploit the multi-core platform, this paper presents experimental evaluation to understand partitioning performance on small-scaled heterogeneous multi-core clusters. With results and analyses gathered, we propose a hierarchical framework for resource-aware graph partitioning on heterogeneous multi-core clusters. Preliminary evaluation demonstrates the potential of the framework and motivates directions for incorporating application requirements into graph partitioning.