Fermilab Computing Division

CS Document 4531-v1

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

Document #:
CS-doc-4531-v1
Document type:
Conference
Submitted by:
Wenji Wu
Updated by:
Wenji Wu
Document Created:
06 Nov 2011, 16:53
Contents Revised:
06 Nov 2011, 16:53
Metadata Revised:
06 Nov 2011, 16:53
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Abstract:
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.
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Publication Information:
the 37th IEEE Local Computer Networks
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