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dc.contributor.advisorAnant Agarwal
dc.contributor.authorHoffmann, Henryen_US
dc.contributor.authorMaggio, Martinaen_US
dc.contributor.authorSantambrogio, Marco D.en_US
dc.contributor.authorLeva, Albertoen_US
dc.contributor.authorAgarwal, Ananten_US
dc.contributor.otherComputer Architectureen
dc.date.accessioned2011-03-24T21:15:14Z
dc.date.available2011-03-24T21:15:14Z
dc.date.issued2011-03-24
dc.identifier.urihttp://hdl.handle.net/1721.1/61950
dc.description.abstractThis paper presents SEEC, a self-aware programming model, designed to reduce programming effort in modern multicore systems. In the SEEC model, application programmers specify application goals and progress, while systems programmers separately specify actions system software and hardware can take to affect an application (e.g. resource allocation). The SEEC runtime monitors applications and dynamically selects actions to meet application goals optimally (e.g. meeting performance while minimizing power consumption). The SEEC runtime optimizes system behavior for the application rather than requiring the application programmer to optimize for the system. This paper presents a detailed discussion of the SEEC model and runtime as well as several case studies demonstrating their benefits. SEEC is shown to optimize performance per Watt for a video encoder, find optimal resource allocation for an application with complex resource usage, and maintain the goals of multiple applications in the face of environmental fluctuations.en_US
dc.format.extent14 p.en_US
dc.relation.ispartofseriesMIT-CSAIL-TR-2011-016
dc.subjectSelf-adaptive, self-optimizing, self-tuning, self-*, power-awareen_US
dc.titleSEEC: A Framework for Self-aware Management of Multicore Resourcesen_US


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