Title:
Coordinated power management in heterogeneous processors

dc.contributor.advisor Yalamanchili, Sudhakar
dc.contributor.author Paul, Indrani
dc.contributor.committeeMember Mukhopadhyay, Saibal
dc.contributor.committeeMember Kim, Hyesoon
dc.contributor.committeeMember Schawn, Karsten
dc.contributor.committeeMember John, Lizy K.
dc.contributor.department Electrical and Computer Engineering
dc.date.accessioned 2015-06-08T18:34:59Z
dc.date.available 2015-06-08T18:34:59Z
dc.date.created 2015-05
dc.date.issued 2015-03-31
dc.date.submitted May 2015
dc.date.updated 2015-06-08T18:34:59Z
dc.description.abstract Coordinated Power Management in Heterogeneous Processors Indrani Paul 164 pages Directed by Dr. Sudhakar Yalamanchili With the end of Dennard scaling, the scaling of device feature size by itself no longer guarantees sustaining the performance improvement predicted by Moore’s Law. As industry moves to increasingly small feature sizes, performance scaling will become dominated by the physics of the computing environment and in particular by the transient behavior of interactions between power delivery, power management and thermal fields. Consequently, performance scaling must be improved by managing interactions between physical properties, which we refer to as processor physics, and system level performance metrics, thereby improving the overall efficiency of the system. The industry shift towards heterogeneous computing is in large part motivated by energy efficiency. While such tightly coupled systems benefit from reduced latency and improved performance, they also give rise to new management challenges due to phenomena such as physical asymmetry in thermal and power signatures between the diverse elements and functional asymmetry in performance. Power-performance tradeoffs in heterogeneous processors are determined by coupled behaviors between major components due to the i) on-die integration, ii) programming model and the iii) processor physics. Towards this end, this thesis demonstrates the needs for coordinated management of functional and physical resources of a heterogeneous system across all major compute and memory elements. It shows that the interactions among performance, power delivery and different types of coupling phenomena are not an artifact of an architecture instance, but is fundamental to the operation of many core and heterogeneous architectures. Managing such coupling effects is a central focus of this dissertation. This awareness has the potential to exert significant influence over the design of future power and performance management algorithms. The high-level contributions of this thesis are i) in-depth examination of characteristics and performance demands of emerging applications using hardware measurements and analysis from state-of-the-art heterogeneous processors and high-performance GPUs, ii) analysis of the effects of processor physics such as power and thermals on system level performance, iii) identification of a key set of run-time metrics that can be used to manage these effects, and iv) development and detailed evaluation of online coordinated power management techniques to optimize system level global metrics in heterogeneous CPU-GPU-memory processors.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/53547
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Coordinated power management
dc.subject Power management
dc.subject Heterogeneous processors
dc.title Coordinated power management in heterogeneous processors
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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