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dc.contributor.advisorMalony, Allenen_US
dc.contributor.authorChaimov, Nicholasen_US
dc.creatorChaimov, Nicholasen_US
dc.date.accessioned2012-10-26T04:04:56Z
dc.date.available2012-10-26T04:04:56Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/1794/12434
dc.description.abstractEfficient large-scale scientific computing requires efficient code, yet optimizing code to render it efficient simultaneously renders the code less readable, less maintainable, less portable, and requires detailed knowledge of low-level computer architecture, which the developers of scientific applications may lack. The necessary knowledge is subject to change over time as new architectures, such as GPGPU architectures like CUDA, which require very different optimizations than CPU-targeted code, become more prominent. The development of scientific cloud computing means that developers may not even know what machine their code will be running on when they are developing it. This work takes steps towards automating the generation of code variants which are automatically optimized for both execution environment and input dataset. We demonstrate that augmenting an autotuning framework with a performance database which captures metadata about environment and input and performing decision tree learning over that data can help more fully automate the process of enhancing software performance.en_US
dc.language.isoen_USen_US
dc.publisherUniversity of Oregonen_US
dc.rightsAll Rights Reserved.en_US
dc.subjectautotuningen_US
dc.subjectoptimizationen_US
dc.subjectspecializationen_US
dc.titleA Framework for Automated Generation of Specialized Function Variantsen_US
dc.typeElectronic Thesis or Dissertationen_US


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