Graphicionado: Accelerate Graph Analytics

Process graph analytics workloads one to two orders more performant and energy efficient than a 16-core Haswell Xeon server.

Graphs are one of the key data structures for many real-world computing applications and the importance of graph analytics is ever-growing. While existing software graph processing frameworks improve programmability of graph analytics, underlying general purpose processors still limit the performance and energy efficiency of graph analytics.

We architect a domain-specific accelerator, Graphicionado, for high performance, energy-efficient processing of graph analytics workloads. For efficient graph analytics processing, Graphicionado exploits not only data structure-centric datapath specialization, but also memory subsystem specialization, all the while taking advantage of the parallelism inherent in this domain. Graphicionado augments the vertex programming paradigm, allowing different graph analytics applications to be mapped to the same accelerator framework, while maintaining flexibility through a small set of reconfigurable blocks.

Our results show that Graphicionado achieves a 1.76−6.54× speedup while consuming 50−100× less energy compared to a state-of-the-art software graph analytics processing framework executing 32 threads on a 16-core Haswell Xeon processor.


Tae Jun Ham, Lisa Wu, Narayanan Sundaram, Nadathur Satish, and Margaret Martonosi, "Graphicionado: A High-Performance and Energy-Efficient Accelerator for Graph Analytics". IEEE/ACM International Symposium on Microarchitecture (MICRO) 2016.