Massively-Parallel Graph Processing

The goal of this project is to understand the challenges in porting graph algorithms to commodity, hybrid platforms; platforms that consist of processors optimized for sequential processing and accelerators optimized for massively-parallel processing. This study fills the gap between current graph processing platforms that are either expensive (e.g., supercomputers) or inefficient (e.g., commodity clusters). Our hypothesis is that hybrid platforms (e.g., GPU-supported clusters) can bridge the performance-cost chasm, and offer an attractive graph-processing solution for many graph-based applications such as social networks and web analysis. This work presents the first step towards designing Totem – a graph-processing framework that leverages massively parallel hybrid platforms. In particular, we design, implement, and evaluate core graph algorithms (i.e., BFS, Dijkstra’s algorithm, and PageRank). Also, we discuss the future work based on the current experience provided by these initial implementations.