Exploring Core and Cache Hierarchy Bottlenecks in Graph Processing Workloads

Abstract

Graph processing is an important analysis technique for a wide range of big data problems. The ability to explicitly represent relationships between entities gives graph analytics significant performance advantage over traditional relational databases. In this paper, we perform an in-depth data-aware characterization of graph processing workloads on a simulated multi-core architecture, find bottlenecks in the core and the cache hierarchy that are not highlighted by previous characterization work, and analyze the behavior of the specific application data type causing the corresponding bottleneck. We find that load-load dependency chains involving different application data types form the primary bottleneck in achieving a high memory-level parallelism in graph processing workloads. We also observe that the private L2 cache has a negligible contribution to performance, whereas the shared L3 cache has higher performance sensitivity. In addition, we present a study on the effectiveness of several replacement policies. Finally, we study the relationship between different graph algorithms and the access volumes to the different data types. Overall, we provide useful insights and guidelines toward developing a more optimized CPU-based architecture for high performance graph processing.