RT Journal Article
T1 Towards automatic parallelization of stream processing applications
A1 Dolz Zaragoza, Manuel Francisco
A1 Río Astorga, David del
A1 González Fernández, Francisco Javier
A1 Carretero Pérez, Jesús
A1 García Sánchez, José Daniel
AB 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.
PB IEEE Xplore
SN 2169-3536
YR 2018
FD 2018-07-11
LK https://hdl.handle.net/10016/31718
UL https://hdl.handle.net/10016/31718
LA eng
NO This work was supported in part by the Spanish Ministerio de Economía y Competitividad through the Project Toward Uni cation of HPCand Big Data Paradigms under Grant TIN2016-79637-P and in part by the EU Project RePhrase: REfactoring Parallel HeterogeneousResource-Aware Applications under Grant ICT 644235.
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RD 19 may. 2024