Towards Automatic Parallelization of Stream Processing Applications

Parallelizing and optimizing codes for recent multi-/many-core processors have been recognized to be a complex task. For this reason, strategies to automatically transform sequential codes into parallel and discover optimization opportunities are crucial to relieve the burden to developers. In this...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.39944-39961
Main Authors: Dolz, Manuel F., Del Rio Astorga, David, Fernandez, Javier, Garcia, J. Daniel, Carretero, Jesus
Format: Article
Language:English
Subjects:
automatic parallelization
C++ languages
Computer vision
Concurrent computing
load-balanced pipeline
Microprocessors
Optimization
parallel patterns
Parallel processing
Pipelines
Pipelining (computers)
Programming
Refactoring framework
Task analysis
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Summary:Parallelizing and optimizing codes for recent multi-/many-core processors have been recognized to be a complex task. For this reason, strategies to automatically transform sequential codes into parallel and discover optimization opportunities are crucial to relieve the burden to developers. In this paper, we present a compile-time framework to (semi) automatically find parallel patterns (Pipeline and Farm) and transform sequential streaming applications into parallel using GrPPI, a generic parallel pattern interface. This framework uses a novel pipeline stage-balancing technique which provides the code generator module with the necessary information to produce balanced pipelines. The evaluation, using a synthetic video benchmark and a real-world computer vision application, demonstrates that the presented framework is capable of producing parallel and optimized versions of the application. A comparison study under several thread-core oversubscribed conditions reveals that the framework can bring comparable performance results with respect to the Intel TBB programming framework.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2855064