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Energy-Efficient Static Scheduling of Streaming Task Collections with Malleable Tasks
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, The Institute of Technology. (PELAB)ORCID iD: 0000-0001-5241-0026
FernUniversität in Hagen, Germany. (Parallelität und VLSI)
FernUniversität in Hagen, Germany. (Parallelität und VLSI)
2013 (English)In: Proc. 25th PARS-Workshop, Gesellschaft für Informatik, 2013, 37-46 p.Conference paper (Refereed)
Abstract [en]

We investigate the energy-efficiency of streaming task collections with parallelizable or malleable tasks on a manycore processor with frequency scaling. Streaming task collections differ from classical task sets in that all tasks are running concurrently, so that cores typically run several tasks that are scheduled round-robin on user level. A stream of data flows through the tasks and intermediate results are forwarded to other tasks like in a pipelined task graph. We first show the equivalence of task mapping for streaming task collections and normal task collections in the case of continuous frequency scaling, under reasonable assumptions for the user-level scheduler, if a makespan, i.e. a throughput requirement of the streaming application, is given and the energy consumed is to be minimized. We then show that in the case of discrete  frequency scaling, it might be necessary for processors to switch frequencies, and that idle times still can occur, in contrast to continuous frequency scaling. We formulate the mapping of (streaming) task collections on a manycore processor with discrete frequency levels as an integer linear program. Finally, we propose two heuristics to reduce energy consumption compared to the previous results by improved load balancing through the parallel execution of a parallelizable task. We evaluate the effects of the heuristics analytically and experimentally on the Intel SCC.

Place, publisher, year, edition, pages
Gesellschaft für Informatik, 2013. 37-46 p.
, PARS-Mitteilungen, ISSN 0177-0454 ; 30
Keyword [en]
task scheduling, malleable tasks, energy efficient computing, parallel computing, task mapping, frequency scaling, multicore processor
National Category
Computer Science
URN: urn:nbn:se:liu:diva-102582OAI: diva2:679373
25th PARS Workshop, Erlangen, Germany, 11-12 April 2013
Integrated Software Pipelining (VR)SeRC - OpCoReS
Swedish Research Council, 621-2009-4449Swedish e‐Science Research Center, OpCoReS
Available from: 2013-12-15 Created: 2013-12-15 Last updated: 2014-10-08

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ReferencesLink to record
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