  Science Information Network (SINET) is a Japanese academic backbone network
for more than 800 research institutions and universities. In this paper, we
present a multi-GPU-driven pipeline for handling huge session data of SINET.
Our pipeline consists of ELK stack, multi-GPU server, and Splunk. A multi-GPU
server is responsible for two procedures: discrimination and histogramming.
Discrimination is dividing session data into ingoing/outgoing with subnet mask
calculation and network address matching. Histogramming is grouping
ingoing/outgoing session data into bins with map-reduce. In our architecture,
we use GPU for the acceleration of ingress/egress discrimination of session
data. Also, we use a tiling design pattern for building a two-stage map-reduce
of CPU and GPU. Our multi-GPU-driven pipeline has succeeded in processing huge
workloads of about 1.2 to 1.6 billion session streams (500GB-650GB) within 24
hours.
