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dc.contributor.advisorFranke, Bjornen
dc.contributor.advisorMahesh, Marinaen
dc.contributor.authorFenacci, Damonen
dc.date.accessioned2012-08-07T13:32:08Z
dc.date.available2012-08-07T13:32:08Z
dc.date.issued2012-06-25
dc.identifier.urihttp://hdl.handle.net/1842/6210
dc.description.abstractThis work approaches the little studied topic of compiler optimisations directed to network applications. It starts by investigating if there exist any fundamental differences between application domains that justify the development and tuning of domain-specific compiler optimisations. It shows an automated approach that is capable of identifying domain-specific workload characterisations and presenting them in a readily interpretable format based on decision trees. The generated workload profiles summarise key resource utilisation issues and enable compiler engineers to address the highlighted bottlenecks. By applying this methodology to data intensive network infrastructure application it shows that data organisation is the key obstacle to overcome in order to achieve high performance. It therefore proposes and evaluates three specialised data transformations (structure splitting, array regrouping, and software caching) against the industrial EEMBC networking benchmarks and real-world data sets. It also demonstrates on one hand that speedups of up to 2.62 can be achieved, but on the other that no single solution performs equally well across different network traffic scenarios. Hence, to address this issue, an adaptive software caching scheme for high frequency route lookup operations is introduced and its effectiveness evaluated one more time against EEMBC networking benchmarks and real-world data sets achieving speedups of up to 3.30 and 2.27. The results clearly demonstrate that adaptive data organisation schemes are necessary to ensure optimal performance under varying network loads. Finally this research addresses another issue introduced by data transformations such as array regrouping and software caching, i.e. the need for static analysis to allow efficient resource allocation. This thesis proposes a static code analyser that allows the automatic resource analysis of source code containing lists and tree structures. The tool applies a combination of amortised analysis and separation logic methodology to real code and is able to evaluate type and resource usage of existing data structures, which can be used to compute global resource consumption values for full data intensive network applications.en
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en
dc.language.isoen
dc.publisherThe University of Edinburghen
dc.relation.hasversionDamon Fenacci, Bj¨orn Franke and John Thompson, Automatic Identification of Tuning Opportunities for Domain-Specific Compilers Using Decision Trees for Data Mining, Proceedings of the 13th International Workshop on Software and Compilers for Embedded Systems (SCOPES), June 2010en
dc.relation.hasversionDamon Fenacci and Bj¨orn Franke, Empirical evaluation of data transformations for network infrastructure applications, International Conference on Embedded Computer Systems: Architectures, MOdeling and Simulation (SAMOS X), July 2010en
dc.relation.hasversionGiacomo Bernardi, Matt Calder, Damon Fenacci, Alex Macmillan and Mahesh Marina, Stix: A Goal-Oriented Distributed Management System for Large- Scale Broadband Wireless Access Networks, International Conference on Mobile Computing and Networking (MobiCom), September 2010en
dc.relation.hasversionKenneth McKenzie and Damon Fenacci, Static Resource Analysis for Java Bytecode Using Amortisation and Separation Logic, 6th Workshop on Bytecode Semantics, Verification, Analysis and Transformation (Bytecode), March 2011en
dc.relation.hasversionGiacomo Bernardi, Damon Fenacci, Mahesh Marina and Dimitrios Pezaros, Large-Scale Broadband Quality Assessment Using Distributed Monitoring, IFIP/TC6 Networking 2012, May 2012.en
dc.subjectcompileren
dc.subjectnetworkingen
dc.subjectoptimisationsen
dc.titleCompiler-driven data layout transformations for network applicationsen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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