Adapting the Streaming Video Based on the Estimated Position of the Region of Interest

Aziz, Hussein Muzahim; Fiedler, Markus; Grahn, Håkan; Lundberg, Lars

doi:10.1109/sitis.2012.11

Cited by 2 publications

(3 citation statements)

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“…SCHEDULING have shortly described the increased flexibility for dynamic crown rescaling due to the crown structure constraints, which allows to easily adapt to fluctuations in streaming task load at runtime. This can be useful to handle applications whose workload can change dynamically, depending on the data of the application, e.g., video decoding [9], using quasi-static scheduling [6,128]. We have also sketched ways to relax the restrictions of the crown shape, and showed that, in the worst case, an optimal crown schedule is off from an optimal general schedule by a factor of 3.7 for a realistic platform model.…”

Section: Resultsmentioning

confidence: 99%

“…On Listing C.7, we remove output lines where the instance numbers increase linearly. We see that there is no output line for instance 6,7,8,9 or 10. This is because these lines correspond to running square root with a base value other than 2, which is not permitted by Line 12 of Listing C. …”

Section: Crown Schedulers Chapter 7 Softwarementioning

confidence: 96%

“…. If we replace lines 7 and 8 of Listing C.5 by Listing C.8 and run Freja again for the same experiment name, Freja find automatically what experiments were already successfully run and tries again to run all the ones that failed in previous attempts; we obtain the trace in Listing C. 9. We see that Freja Skipped all instances that ran correctly in the previous attempt, and C.3.…”

Section: C21 Preparing Environment Settingsmentioning

confidence: 99%

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Algorithms and Framework for Energy Efficient Parallel Stream Computing on Many-Core Architectures

Melot¹

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The rise of many-core processor architectures in the market answers to a constantly growing need of processing power to solve more and more challenging problems such as the ones in computing for big data. Fast computation is more and more limited by the very high power required and the management of the considerable heat produced. Many programming models compete to take profit of many-core architectures to improve both execution speed and energy consumption, each with their advantages and drawbacks. The work described in this thesis is based on the dataflow computing approach and investigates the benefits of a carefully pipelined execution of streaming applications, focusing in particular on off-and on-chip memory accesses. As case study, we implement classic and on-chip pipelined versions of mergesort for Intel SCC and Xeon. We see how the benefits of the on-chip pipelining technique are bounded by the underlying architecture, and we explore the problem of fine tuning streaming applications for many-core architectures to optimize for energy given a throughput budget. We propose a novel methodology to compute schedules optimized for energy efficiency given a fixed throughput target. We introduce Drake, derived from Schedeval, a tool that generates pipelined applications for Many-Core architectures and allows the performance testing in time or energy of their static schedule. We show that streaming applications based on Drake compete with specialized implementations and we use Schedeval to demonstrate performance differences between schedules that are otherwise considered as equivalent by a simple model. This work has been supported in parts by CUGS (the Graduate School in Computer Science, Sweden), Vetenskapsrådet, SeRC and EU FP7 EXCESS. Department of Computer and Information ScienceLinköping University SE-581 83 Linköping, Sweden Acknowledgements I would like to thank all members of PELAB for the inspiring and stimulating working environment as well as the passionate discussions around coffee or tea. In particular, my thanks to Kristian Sandahl for his efforts at maintening a strong group culture. Warm thanks to Christoph Kessler, for all fruitful discussions and ideas when my imagination came short, for his support and patience when results came late and for entrusting me with opportunities and responsibilities that taught me valuable experiences. Many thanks to Jörg Keller for sharing his ideas in details about my work, and for always carefully reviewing and challenging any hypothesis I proposed, as it repeatedly resulted in strengthening good ideas and discarding bad ones. I would like to thank all students who participated in the work, providing a precious help to progress. I am grateful to Intel for providing the Single Chip Cloud computer research prototype and their efforts to help in the numerous moments where nothing worked. Many thanks to the National Supercomputer Center (NSC) for providing powerful computing means to run my experiments, TUS for always providing a quick and precious technica...

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Section: Resultsmentioning

confidence: 99%

Section: Crown Schedulers Chapter 7 Softwarementioning

confidence: 96%

Section: C21 Preparing Environment Settingsmentioning

confidence: 99%

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