Using data partitioning to implement a parallel assembler
PPEALS '88 Proceedings of the ACM/SIGPLAN conference on Parallel programming: experience with applications, languages and systems
Parallel compilation for a parallel machine
PLDI '89 Proceedings of the ACM SIGPLAN 1989 Conference on Programming language design and implementation
Upper Bounds for Speedup in Parallel Parsing
Journal of the ACM (JACM)
The theory of parsing, translation, and compiling
The theory of parsing, translation, and compiling
On Building Parallel & Grid Applications: Component Technology and Distributed Services
CLADE '04 Proceedings of the 2nd International Workshop on Challenges of Large Applications in Distributed Environments
A Metadata Catalog Service for Data Intensive Applications
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
A Benchmark Suite for SOAP-based Communication in Grid Web Services
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
A Table-Driven Streaming XML Parsing Methodology for High-Performance Web Services
ICWS '06 Proceedings of the IEEE International Conference on Web Services
Benchmarking XML processors for applications in grid web services
Proceedings of the 2006 ACM/IEEE conference on Supercomputing
A Static Load-Balancing Scheme for Parallel XML Parsing on Multicore CPUs
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
A Parallel Approach to XML Parsing
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
Memory-side acceleration for XML parsing
NPC'11 Proceedings of the 8th IFIP international conference on Network and parallel computing
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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.