EXPLORING HIGH PERFORMANCE AND ENERGY EFFICIENT GRAPH PROCESSING ON GPU

Parallel graph processing is central to analytical computer science applications, and GPUs have proven to be an ideal platform for parallel graph processing. Existing GPU graph processing frameworks present performance improvements but often neglect two issues: the unpredictability of a given input graph and the energy consumption of the graph processing. Our prototype software, EEGraph (Energy Efficiency of Graph processing), is a flexible system consisting of several graph processing algorithms with configurable parameters for vertex update synchronization, vertex activation, and memory management along with a lightweight software-based GPU energy measurement scheme. We observe relationships between different configurations of our software, performance, and GPU energy for processing in-memory and out-of-memory graphs. The ideal parameters are discovered for specific input graphs by analyzing the observed relationships. We also present the utility of subgraph generation to predict the performance and energy consumption of complete graph configurations. EEGraph improves upon state-of-the-art GPU-based graph processing software by 2.08 times for performance and 1.60 times for GPU energy for processing in-memory graph datasets. Additionally, EEGraph improves upon the state-of-the-art by 3.30 times for performance and 1.63 times for GPU energy for processing large out-of-memory graph datasets