2 Search Results for "Zheng, Yan"


Document
Optimal Dataflow Scheduling on a Heterogeneous Multiprocessor With Reduced Response Time Bounds

Authors: Zheng Dong, Cong Liu, Alan Gatherer, Lee McFearin, Peter Yan, and James H. Anderson

Published in: LIPIcs, Volume 76, 29th Euromicro Conference on Real-Time Systems (ECRTS 2017)


Abstract
Heterogeneous computing platforms with multiple types of computing resources have been widely used in many industrial systems to process dataflow tasks with pre-defined affinity of tasks to subgroups of resources. For many dataflow workloads with soft real-time requirements, guaranteeing fast and bounded response times is often the objective. This paper presents a new set of analysis techniques showing that a classical real-time scheduler, namely earliest-deadline first (EDF), is able to support dataflow tasks scheduled on such heterogeneous platforms with provably bounded response times while incurring no resource capacity loss, thus proving EDF to be an optimal solution for this scheduling problem. Experiments using synthetic workloads with widely varied parameters also demonstrate that the magnitude of the response time bounds yielded under the proposed analysis is reasonably small under all scenarios. Compared to the state-of-the-art soft real-time analysis techniques, our test yields a 68% reduction on response time bounds on average. This work demonstrates the potential of applying EDF into practical industrial systems containing dataflow-based workloads that desire guaranteed bounded response times.

Cite as

Zheng Dong, Cong Liu, Alan Gatherer, Lee McFearin, Peter Yan, and James H. Anderson. Optimal Dataflow Scheduling on a Heterogeneous Multiprocessor With Reduced Response Time Bounds. In 29th Euromicro Conference on Real-Time Systems (ECRTS 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 76, pp. 15:1-15:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)


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@InProceedings{dong_et_al:LIPIcs.ECRTS.2017.15,
  author =	{Dong, Zheng and Liu, Cong and Gatherer, Alan and McFearin, Lee and Yan, Peter and Anderson, James H.},
  title =	{{Optimal Dataflow Scheduling on a Heterogeneous Multiprocessor With Reduced Response Time Bounds}},
  booktitle =	{29th Euromicro Conference on Real-Time Systems (ECRTS 2017)},
  pages =	{15:1--15:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-037-8},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{76},
  editor =	{Bertogna, Marko},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2017.15},
  URN =		{urn:nbn:de:0030-drops-71565},
  doi =		{10.4230/LIPIcs.ECRTS.2017.15},
  annote =	{Keywords: Real-time Scheduling, schedulability, heterogeneous multiprocessor}
}
Document
Geometric Inference on Kernel Density Estimates

Authors: Jeff M. Phillips, Bei Wang, and Yan Zheng

Published in: LIPIcs, Volume 34, 31st International Symposium on Computational Geometry (SoCG 2015)


Abstract
We show that geometric inference of a point cloud can be calculated by examining its kernel density estimate with a Gaussian kernel. This allows one to consider kernel density estimates, which are robust to spatial noise, subsampling, and approximate computation in comparison to raw point sets. This is achieved by examining the sublevel sets of the kernel distance, which isomorphically map to superlevel sets of the kernel density estimate. We prove new properties about the kernel distance, demonstrating stability results and allowing it to inherit reconstruction results from recent advances in distance-based topological reconstruction. Moreover, we provide an algorithm to estimate its topology using weighted Vietoris-Rips complexes.

Cite as

Jeff M. Phillips, Bei Wang, and Yan Zheng. Geometric Inference on Kernel Density Estimates. In 31st International Symposium on Computational Geometry (SoCG 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 34, pp. 857-871, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2015)


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@InProceedings{phillips_et_al:LIPIcs.SOCG.2015.857,
  author =	{Phillips, Jeff M. and Wang, Bei and Zheng, Yan},
  title =	{{Geometric Inference on Kernel Density Estimates}},
  booktitle =	{31st International Symposium on Computational Geometry (SoCG 2015)},
  pages =	{857--871},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-83-5},
  ISSN =	{1868-8969},
  year =	{2015},
  volume =	{34},
  editor =	{Arge, Lars and Pach, J\'{a}nos},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.SOCG.2015.857},
  URN =		{urn:nbn:de:0030-drops-51349},
  doi =		{10.4230/LIPIcs.SOCG.2015.857},
  annote =	{Keywords: topological data analysis, kernel density estimate, kernel distance}
}
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