Software Pipelining for Stream Programs on Resource Constrained Multicore Architectures

Stream programming model has been productively applied to a number of important application domains. Software pipelining is an important code scheduling technique for stream programs. However, the multicore evolution has presented a new dimension of challenges: that is how to orchestrate the best so...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems Vol. 23; no. 12; pp. 2338 - 2350
Main Authors: Haitao Wei, Junqing Yu, Huafei Yu, Mingkang Qin, Gao, G. R.
Format: Journal Article
Language:English
Published: New York IEEE 01.12.2012
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computer networks
Computer programs
Constraints
Mathematical models
Memory management
Multicore
Multicore processing
Pipeline processing
Pipelining (computers)
resource constrained
Resource management
Schedules
Software
software pipelining
stream programs
Streams
Studies
ISSN:1045-9219, 1558-2183
Online Access:Get full text
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Summary:Stream programming model has been productively applied to a number of important application domains. Software pipelining is an important code scheduling technique for stream programs. However, the multicore evolution has presented a new dimension of challenges: that is how to orchestrate the best software pipelining schedule in the face of resource constrained architectures (e.g., number of cores, available memory, and bandwidth)? In this paper, we proposed a new solution methodology to address the problem above. Our main contributions include the following. A unified Integer Linear Programming (ILP) formulation has been proposed that combines the requirement of both rate-optimal software pipelining and the minimization of intercore communication overhead. Next, an extended formulation has been proposed to formulate the schedule under memory size constrained systems. It orchestrates the rate-optimal software pipelining execution for stream programs with strict memory, processor cores, and communication constraints. A solution testbed has been implemented for the proposed problem formulations. This has been realized by extending the Brook programming environment with our software pipelining support-named DFBrook. An experimental study has been conducted to verify the effectiveness of the proposed solutions.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2012.41