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Algorithms and Framework for Energy Efficient Parallel Stream Computing on Many-Core Architectures
Linköpings universitet, Institutionen för datavetenskap, Programvara och system. Linköpings universitet, Tekniska fakulteten. (PELAB)ORCID-id: 0000-0002-1940-3331
2016 (Engelska)Doktorsavhandling, monografi (Övrigt vetenskapligt)
Abstract [en]

The rise of many-core processor architectures in the market answers to a constantly growing need of processing power to solve more and more challenging problems such as the ones in computing for big data. Fast computation is more and more limited by the very high power required and the management of the considerable heat produced. Many programming models compete to take profit of many-core architectures to improve both execution speed and energy consumption, each with their advantages and drawbacks. The work described in this thesis is based on the dataflow computing approach and investigates the benefits of a carefully pipelined execution of streaming applications, focusing in particular on off- and on-chip memory accesses. As case study, we implement classic and on-chip pipelined versions of mergesort for Intel SCC and Xeon. We see how the benefits of the on-chip pipelining technique are bounded by the underlying architecture, and we explore the problem of fine tuning streaming applications for many-core architectures to optimize for energy given a throughput budget. We propose a novel methodology to compute schedules optimized for energy efficiency given a fixed throughput target. We introduce \emph{Drake}, derived from Schedeval, a tool that generates pipelined applications for Many-Core architectures and allows the performance testing in time or energy of their static schedule. We show that streaming applications based on Drake compete with specialized implementations and we use Schedeval to demonstrate performance differences between schedules that are otherwise considered as equivalent by a simple model.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2016. , 255 s.
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1813
Nationell ämneskategori
Data- och informationsvetenskap
Identifikatorer
URN: urn:nbn:se:liu:diva-132308DOI: 10.3384/diss.diva-132308ISBN: 9789176856239 (tryckt)OAI: oai:DiVA.org:liu-132308DiVA: diva2:1040747
Disputation
2017-01-30, Visionen, House B, Campus Valla, Linköping, 10:15 (Engelska)
Opponent
Handledare
Forskningsfinansiär
VetenskapsrådetSwedish e‐Science Research Center
Anmärkning

This thesis has also been funded by CUGS, Graduate School in Computer Science and FP7 EXCESS.

The electronic version has been corrected. See the published errata list.

Tillgänglig från: 2016-12-14 Skapad: 2016-10-28 Senast uppdaterad: 2017-02-08Bibliografiskt granskad

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