Towards Automatic Parallelization of Stream Processing Applications | Zendy

Manuel F. Dolz | Zendy; David Del Rio Astorga | Zendy; Javier Fernandez | Zendy; J. Daniel Garcia | Zendy; Jesus Carretero | Zendy

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Towards Automatic Parallelization of Stream Processing Applications

Author(s) -

Manuel F. Dolz,

David Del Rio Astorga,

Javier Fernandez,

J. Daniel Garcia,

Jesus Carretero

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2855064

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

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.

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