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arXiv · 2106.12863

Choice of Parallelism: Multi-GPU Driven Pipeline for Huge Academic Backbone Network

Ruo Ando·Youki Kadobayashi·Hiroki Takakura
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Abstract

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.

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Ruo Ando, Youki Kadobayashi, Hiroki Takakura. 2021-06-24. Choice of Parallelism: Multi-GPU Driven Pipeline for Huge Academic Backbone Network. https://doi.org/10.1080/17445760.2021.1941009

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