5067

G-NetMon: A GPU-accelerated Network Performance Monitoring System for Large Scale Scientific Collaborations

Wenji Wu, Phil DeMar, Don Holmgren, Amitoj Singh, Ruth Pordes
Computing Division, Fermilab, Batavia, IL 60510, USA
arXiv:1108.1785v1 [cs.NI] (8 Aug 2011)

@article{2011arXiv1108.1785W,

   title={G-NetMon: A GPU-accelerated Network Performance Monitoring System for Large Scale Scientific Collaborations},

   author={Wu, W. and DeMar, P. and Holmgren, D. and Singh, A.},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1108.1785},

   primaryClass={"cs.NI"},

   keywords={Networking and Internet Architecture},

   year={2011},

   month={aug}

}

Download Download (PDF)   View View   Source Source   

851

views

Network traffic is difficult to monitor and analyze, especially in high-bandwidth networks. Performance analysis, in particular, presents extreme complexity and scalability challenges. GPU (Graphics Processing Unit) technology has been utilized recently to accelerate general purpose scientific and engineering computing. GPUs offer extreme thread-level parallelism with hundreds of simple cores. Their data-parallel execution model can rapidly solve large problems with inherent data parallelism. At Fermilab, we have prototyped a GPU-accelerated network performance monitoring system, called G-NetMon, to support large-scale scientific collaborations. In this work, we explore new opportunities in network traffic monitoring and analysis with GPUs. Our system exploits the data parallelism that exists within network flow data to provide fast analysis of bulk data movement between Fermilab and collaboration sites. Experiments demonstrate that our G-NetMon can rapidly detect sub-optimal bulk data movements.
No votes yet.
Please wait...

* * *

* * *

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: