Adaptive relaxed synchronization through the use of supervised learning methods Abstract   Several authors have proposed the use of relaxed synchronization to speed up the execution of parallel applications that admit tradeoffs between quality and execution time. However, most of these works propose the complete removal of synchronization primitives and do not anticipate the quality of the results to be obtained with different input data. In this paper, we propose a novel strategy for relaxing synchronization, evaluating the feasibility of using supervised learning methods to ensure that the relaxed synchronization technique provides results within acceptable limits of error. We use a varied set of program inputs to create a control base, providing data for the training of supervised learning methods. When the user wishes to execute hisher application with new input data (in the same execution environment), the trained classification algorithm will suggest the relax factor that is best suited for the triple applicationinputexecution environment. Using this methodology, we obtained a gain of 3.5x for the Kmeans algorithm applied to videos while maintaining the desired quality rate.

Systematic benchmarking for reproducibility of computer vision algorithms for realtime systems: The example of optic flow estimation Until now there have been few formalized methods for conducting systematic benchmarking aiming at reproducible results when it comes to computer vision algorithms. This is evident from lists of algorithms submitted to prominent datasets, authors of a novel method in many cases primarily state the performance of their algorithms in relation to a shallow description of the hardware system where it was evaluated. There are significant problems linked to this nonsystematic approach of reporting performance, especially when comparing different approaches and when it comes to the reproducibility of claimed results. Furthermore how to conduct retrospective performance analysis such as an algorithmxe2x80x99s suitability for embedded realtime systems over time with underlying hardware and software changes in place. This paper proposes and demonstrates a systematic way of addressing such challenges by adopting containerization of software aiming at formalization and reproducibility of benchmarks. Our results show maintainers of broadly accepted datasets in the computer vision community to strive for systematic comparison and reproducibility of submissions to increase the value and adoption of computer vision algorithms in the future.