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Energy-Efficient Static Scheduling of Streaming Task Collections with Malleable Tasks
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2013
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Gesellschaft für Informatik e.V., Fachgruppe PARS
Zusammenfassung
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 scalingunder reasonable assumptions for the user-level schedulerif a makespani.e. a throughput requirement of the streaming applicationis given and the energy consumed is to be minimized. We then show that in the case of discrete frequency scalingit might be necessary for processors to switch frequenciesand that idle times still can occurin 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. Finallywe propose two heuristics to reduce energy consumption compared to the previous results by improved load bal- ancing through the parallel execution of a parallelizable task. We evaluate the effects of the heuristics analytically and experimentally on the Intel SCC.