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High throughput feature-matching analysis of biological spectral data

Published: 11 November 2006 Publication History

Abstract

n order to overcome the limitations of traditional monolithic firewalls, Pacific Northwest National Laboratory (PNNL) has implemented a Secure Collaboration Zone (SCZ) to provide high performance connectivity using a host-based security model where the network firewall is replaced by a centrally managed host-based firewall on each system in the SCZ. This approach, coupled with intrusion detection capabilities and tight system configuration management controls, eliminates the firewall bottleneck and creates a layered security model that enables secure data transfer at rates which scale to today's system and network capabilities. For SC06 we will be using Polygraph, a high-performance implementation of a statistical and physical model-based feature matching algorithm, to run identification calculations on spectral data resulting from tandem mass-spectrometry experiments. In particular, a compute cluster at SC06 will run Polygraph to access a preprocessed information archive of spectral data located at PNNL in the SCZ.

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cover image ACM Conferences
SC '06: Proceedings of the 2006 ACM/IEEE conference on Supercomputing
November 2006
746 pages
ISBN:0769527000
DOI:10.1145/1188455
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 11 November 2006

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SC '06 Paper Acceptance Rate 54 of 239 submissions, 23%;
Overall Acceptance Rate 1,516 of 6,373 submissions, 24%

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