Massively Parallel Open Source Encoding for Adaptive Streaming

SMPTE 2018 ◽  
2018 ◽  
Author(s):  
Alexander Giladi ◽  
Blake Orth ◽  
Douglas Bay ◽  
David Leach ◽  
Alex Balk
2015 ◽  
Vol 577 ◽  
pp. A7 ◽  
Author(s):  
H. Socas-Navarro ◽  
J. de la Cruz Rodríguez ◽  
A. Asensio Ramos ◽  
J. Trujillo Bueno ◽  
B. Ruiz Cobo

2018 ◽  
Vol 156 (4) ◽  
pp. 160 ◽  
Author(s):  
Nick Hand ◽  
Yu Feng ◽  
Florian Beutler ◽  
Yin Li ◽  
Chirag Modi ◽  
...  

2020 ◽  
Author(s):  
Haipeng Lin ◽  
Xu Feng ◽  
Tzung-May Fu ◽  
Heng Tian ◽  
Yaping Ma ◽  
...  

Abstract. We developed the WRF-GC model, an online coupling of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the GEOS-Chem atmospheric chemistry model, for regional atmospheric chemistry and air quality modeling. Both WRF and GEOS-Chem are open-source and community-supported. WRF-GC provides regional chemistry modellers easy access to the GEOS-Chem chemical module, which is stably-configured, state-of-the-science, well-documented, traceable, benchmarked, actively developed by a large international user base, and centrally managed by a dedicated support team. At the same time, WRF-GC gives GEOS-Chem users the ability to perform high-resolution forecasts and hindcasts for any location and time of interest. WRF-GC is designed to be easy to use, massively parallel, extendable, and easy to update. The WRF-GC coupling structure allows future versions of either one of the two parent models to be immediately integrated into WRF-GC. This enables WRF-GC to stay state-of-the-science with traceability to parent model versions. Physical and chemical state variables in WRF and in GEOS-Chem are managed in distributed memory and translated between the two models by the WRF-GC Coupler at runtime. We used the WRF-GC model to simulate surface PM2.5 concentrations over China during January 22 to 27, 2015 and compared the results to surface observations and the outcomes from a GEOS-Chem nested-grid simulation. Both models were able to reproduce the observed spatiotemporal variations of regional PM2.5, but the WRF-GC model (r = 0.68, bias = 29 %) reproduced the observed daily PM2.5 concentrations over Eastern China better than the GEOS-Chem model did (r = 0.72, bias = 55 %). This was mainly because our WRF-GC simulation, nudged with surface and upper-level meteorological observations, was able to better represent the spatiotemporal variability of the planetary boundary layer heights over China during the simulation period. Both parent models and the WRF-GC Coupler are parallelized across computational cores and can scale to massively parallel architectures. The WRF-GC simulation was three times more efficient than the GEOS-Chem nested-grid simulation at similar resolutions and for the same number of computational cores, owing to the more efficient transport algorithm and the MPI-based parallelization provided by the WRF software framework. WRF-GC scales nearly perfectly up to a few hundred cores on a variety of computational platforms. Version 1.0 of the WRF-GC model supports one-way coupling only, using WRF-simulated meteorological fields to drive GEOS-Chem with no feedbacks from GEOS-Chem. The development of two-way coupling capabilities, i.e., the ability to simulate radiative and microphysical feedbacks of chemistry to meteorology, is under-way. The WRF-GC model is open-source and freely available from http://wrf.geos-chem.org.


2014 ◽  
Vol 185 (12) ◽  
pp. 3358-3371 ◽  
Author(s):  
L. Orgogozo ◽  
N. Renon ◽  
C. Soulaine ◽  
F. Hénon ◽  
S.K. Tomer ◽  
...  

Author(s):  
Fadi P. Deek ◽  
James A. M. McHugh
Keyword(s):  

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