HyperLink   Triangle Counting and Truss Decomposition using FPGA
   
Publication Year:
  2018
Authors
  Sitao Huang, Mohamed Aly, Cong Hao, Qin li, Vikram Sharma, Ketan Date, Jinjun Xiong, Deming Chen, Rakesh Nagi, Wen-mei Hwu
   
Published:
  In Proceedings of the 2018 IEEE High Performance extreme Computing Conference (HPEC)
   
Abstract:

Triangle counting and truss decomposition are two essential procedures in graph analysis. As the scale of graphs grows larger, designing highly efficient graph analysis systems with less power demand becomes more and more urgent. In this paper, we present triangle counting and truss decomposition using a Field-Programmable Gate Array (FPGA). We leverage the flexibility of FPGAs and achieve low-latency high-efficiency implementations. Evaluation on SNAP dataset shows that our triangle counting and truss decomposition implementations achieve 43.5× on average (up to 757.7×) and 6.4× on average (up to 68.0×) higher performance per Watt respectively over GPU solutions.