Adaptive Parallel Graph Mining for CMP Architectures

Mining graph data is an increasingly popular challenge, which has practical applications in many areas, including molecular substructure discovery, web link analysis, fraud detection, and social network analysis. The problem statement is to enumerate all subgraphs occurring in at least σ graphs of a database, where σ is a user specified parameter. Chip Multiprocessors (CMPs) provide true parallel processing, and are expected to become the de facto standard for commodity computing. In this work, building on the state-of-the-art, we propose an efficient approach to parallelize such algorithms for CMPs. We show that an algorithm which adapts its behavior based on the runtime state of the system can improve system utilization and lower execution times. Most notably, we incorporate dynamic state management to allow memory consumption to vary based on availability. We evaluate our techniques on current day shared memory systems (SMPs) and expect similar performance for CMPs. We demonstr...

• Type: Conference
• Year: 2006
• Where: ICDM
• Authors: Gregory Buehrer, Srinivasan Parthasarathy, Yen-Kuang Chen
• Keywords: Data Mining, ICDM 2006, Mining Graph Data, Molecular Substructure Discovery, User Specified Parameter