Dolz Zaragoza, Manuel FranciscoRío Astorga, David delGonzález Fernández, Francisco JavierCarretero Pérez, JesúsGarcía Sánchez, José Daniel2021-01-182021-01-182018-07-11M. F. Dolz, D. Del Rio Astorga, J. Fernández, J. D. García and J. Carretero, "Towards Automatic Parallelization of Stream Processing Applications" in IEEE Access, vol. 6, pp. 39944-39961, 2018, doi: 10.1109/ACCESS.2018.28550642169-3536https://hdl.handle.net/10016/31718Parallelizing 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.engCopyright © 2018, IEEEAtribución 3.0 EspañaRefactoring frameworkAutomatic parallelizationLoad-balanced pipelineParallel patternsresearch articleInformáticahttps://doi.org/10.1109/ACCESS.2018.2855064open access3994439961IEEE Access6AR/0000021931