The energy case for graph processing on hybrid CPU and GPU systems

This paper investigates the power, energy, and performance characteristics of large-scale graph processing on hybrid (i.e., CPU and GPU) single-node systems. Graph processing can be accelerated on hybrid systems by properly mapping the graph-layout to processing units, such that the algorithmic tasks exercise each of the units where they perform best. However, the GPUs have much higher Thermal Design Power (TDP), thus their impact on the overall energy consumption is unclear. Our evaluation using large real-world graphs and synthetic graphs as large as 1 billion vertices and 16 billion edges shows that a hybrid system is efficient in terms of both time-to-solution and energy.