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  1. Proceedings of the 2007 workshop on Service-oriented computing performance: aspects, issues, and approaches (SOCP '07)
  2. Approaching a parallelized XML parser optimized for multi-coreprocessors
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A QoS-based selection approach of autonomic grid services
McGrid: framework for optimizing grid middleware on multi-core processors
Approaching a parallelized XML parser optimized for multi-coreprocessors
Benefits of alternate XML serialization formats in scientific computing
Parallel XML processing by work stealing
Comparing semantic registries: OWLJessKB and InstanceStore
SISC: providing efficient XML-based service-orientation for core OS functionality
Empowering distributed workflow with the data capacitor: maximizing lustre performance across the wide area network
Data transfer performance issues for a web services interface to synchrotron experiments
Comparing the use of bayesian networks and neural networks in response time modeling for service-oriented systems

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Approaching a parallelized XML parser optimized for multi-coreprocessors

Content Provider ACM Digital Library
Author Govindaraju, Madhusudhan Head, Michael R.
Abstract Very large scientific datasets are increasingly becoming available in XML formats. At the same time, multi-core processing is increasingly becoming available on desktop- and laptop-class computing machines. Unfortunately, most XML parsers are still using algorithms that are inherently serial, which show little improvement on newer computing hardware. The current XML implementation landscape does not adequately meet the performance requirements of large scale applications. Thus far, applications using Web services (in the grid community, for example) have largely focused on XML protocol standardization and tool building efforts, and not on addressing the performance bottlenecks when dealing with large volumes of XML data. Generic parallel parsing has been studied in depth over the past thirty years. However, as yet, these results have not been applied to the problem of XML parsing. XML documents have some structural properties that make it more amenable to parallelized parsing than general context-free languages. As has been previously shown, XML parsers spend a large percentage of time tokenizing the input in aninherently serial process, typically running a deterministic finite automaton on the input. Our initial approach, described here, separates the process of parsing the XML from the process of reading the input. We take a well-known high performance parser, Piccolo, and apply two different strategies, Runahead and Piped, and examine the timing of the file read time and hence the overall time to parse large scientific XML files. Under the conditions tested here, performance decreases.
Starting Page 17
Ending Page 22
Page Count 6
File Format PDF
ISBN 9781595937179
DOI 10.1145/1272457.1272460
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2007-06-25
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Parsing Xml Multi-core Parallelization
Content Type Text
Resource Type Article
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