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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Published in:	The Journal of supercomputing Vol. 70; no. 1; pp. 488 - 509
Main Authors:	Su, Yan, Shi, Feng, Talpur, Shahnawaz, Wei, Jin, Tan, Hai
Format:	Journal Article
Language:	English
Published:	Boston Springer US 01.10.2014
Subjects:	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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Abstract	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.
AbstractList	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.
Author	Su, Yan Shi, Feng Wei, Jin Tan, Hai Talpur, Shahnawaz
Author_xml	– sequence: 1 givenname: Yan surname: Su fullname: Su, Yan organization: Beijing Institute of Technology – sequence: 2 givenname: Feng surname: Shi fullname: Shi, Feng organization: Beijing Institute of Technology – sequence: 3 givenname: Shahnawaz surname: Talpur fullname: Talpur, Shahnawaz organization: Beijing Institute of Technology, Mehran University of Engineering and Technology, Jamshoro – sequence: 4 givenname: Jin surname: Wei fullname: Wei, Jin email: Jin.Springer@hotmail.com organization: Beijing Institute of Technology – sequence: 5 givenname: Hai surname: Tan fullname: Tan, Hai organization: Beijing Institute of Technology
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Title	Exploiting controlled-grained parallelism in message-driven stream programs
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