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dc.contributor.authorScogland, Thomas R.en_US
dc.date.accessioned2016-06-05T06:00:13Z
dc.date.available2016-06-05T06:00:13Z
dc.date.issued2014-12-12en_US
dc.identifier.othervt_gsexam:2339en_US
dc.identifier.urihttp://hdl.handle.net/10919/71315
dc.description.abstractHeterogeneity is increasing across all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other coprocessors into everything from cell phones to supercomputers. More quietly it is increasing with the rise of NUMA systems, hierarchical caching, OS noise, and a myriad of other factors. As heterogeneity becomes a fact of life, efficiently managing heterogeneous compute resources is becoming a critical, and ever more complex, task. The focus of this dissertation is to lay the foundation for an autonomic system for heterogeneous computing, employing runtime adaptation to improve performance portability and performance consistency while maintaining or increasing programmability. We investigate heterogeneity arising from a myriad of factors, grouped into the dimensions of locality and capability. This work has resulted in runtime schedulers capable of automatically detecting and mitigating heterogeneity in physically homogeneous systems through MPI and adaptive coscheduling for physically heterogeneous accelerator based systems as well as a synthesis of the two to address multiple levels of heterogeneity as a coherent whole. We also discuss our current work towards the next generation of fine-grained scheduling and synchronization across heterogeneous platforms in the design of a highly-scalable and portable concurrent queue for many-core systems. Each component addresses aspects of the urgent need for automated management of the extreme and ever expanding complexity introduced by heterogeneity.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis Item is protected by copyright and/or related rights. Some uses of this Item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectSchedulingen_US
dc.subjectGraphics Processing Unit (GPU)en_US
dc.subjectOpenMPen_US
dc.titleRuntime Adaptation for Autonomic Heterogeneous Computingen_US
dc.typeDissertationen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Science and Applicationsen_US
dc.contributor.committeechairFeng, Wu-Chunen_US
dc.contributor.committeememberCao, Yongen_US
dc.contributor.committeememberde Supinski, Bronis R.en_US
dc.contributor.committeememberBalaji, Pavanen_US
dc.contributor.committeememberButt, Ali Raza Ashrafen_US


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