Exploiting controlled-grained parallelism in message-driven stream programs

With the increasing amount of parallelism obtainable on multicore platforms, stream programming has been proposed as an effective solution for exposing distributed parallelization. Nonetheless, a pressing demand of scheduling task and data parallelism in stream programming exists that can accomplish...

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Veröffentlicht in:The Journal of supercomputing Jg. 70; H. 1; S. 488 - 509
Hauptverfasser: Su, Yan, Shi, Feng, Talpur, Shahnawaz, Wei, Jin, Tan, Hai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.10.2014
Schlagworte:
Compilers
Computer Science
Interpreters
Processor Architectures
Programming Languages
Multicore
Stream programming
Message-driven execution
Controlled-grained parallelism
C++11
ISSN:0920-8542, 1573-0484
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Zusammenfassung:With the increasing amount of parallelism obtainable on multicore platforms, stream programming has been proposed as an effective solution for exposing distributed parallelization. Nonetheless, a pressing demand of scheduling task and data parallelism in stream programming exists that can accomplish robust multicore performance in the face of varying application characteristics. This paper addresses the problem of scheduling task and data parallelism in stream programming. We present StreamMDE, an asynchronous concurrency stream programming framework which offers a novel parallel programming model for scheduling task and data parallelism in the message-driven execution paradigm. A key property of this framework is exposing controlled-grained parallelism, which allows us to control the granularity of task and data parallelism in stream graph. Our empirical evaluation of StreamMDE shows that higher efficiency of mixed task and data parallelism in stream programming can be exploited with the appropriate granularity control. The framework bridges the gap between the parallel scale and the architecture of stream programs and facilitates in designing and coding stream features in different schedules.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-014-1264-0