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Link to original content: https://unpaywall.org/10.1007/978-3-319-62458-7_17
Energy-Efficiency and Performance Comparison of Aerosol Optical Depth Retrieval on Distributed Embedded SoC Architectures | SpringerLink
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Energy-Efficiency and Performance Comparison of Aerosol Optical Depth Retrieval on Distributed Embedded SoC Architectures

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Scientific Computing and Algorithms in Industrial Simulations

Abstract

The Aerosol Optical Depth (AOD) is a significant optical property of aerosols and is applied to the atmospheric correction of remotely sensed surface features as well as for monitoring volcanic eruptions, forest fires, and air quality in general, as well as gathering data for climate predictions on the basis of observations from satellites. We have developed an AOD retrieval workflow for processing satellite data not only with ordinary CPUs but also with parallel processors and GPU accelerators in a distributed hardware environment. This workflow includes pre-processing procedures which are followed by the runtime dominating main retrieval method.

In this paper, we investigate if and how the main retrieval method can accommodate recent upcoming embedded hardware architectures in the field of high performance computing. We analyze and confirm the achieved performance as well as energy efficiency with real-world data from the moderate-resolution imaging spectro-radiometer (MODIS) and even compare the potential of those new architectures to today’s commonly available HPC hardware. Due to the very low energy intake, such embedded hardware architectures provide a great chance for situations with strong energy constraints like the pre-processing of recorded data on board of satellites.

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References

  1. R.J. Flowerdew, J.D. Haigh, An approximation to improve accuracy in the derivation of surface reflectances from multi-look satellite radiometers. Geophys. Res. Lett. 22, 1693–1696 (1995)

    Article  Google Scholar 

  2. R.C. Levy, S. Mattoo, L.A. Munchak, et al., The Collection 6 MODIS aerosol products over land and ocean. Atmos. Meas. Tech. 6, 2989–3034 (2013)

    Article  Google Scholar 

  3. J. Liu, D. Feld, Y. Xue, J. Garcke, T. Soddemann, Multicore processors and graphics processing unit accelerators for parallel retrieval of aerosol optical depth from satellite data: implementation, performance, and energy efficiency. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8, 2306–2317 (2015)

    Article  Google Scholar 

  4. J. Liu, D. Feld, Y. Xue, et al., An efficient geosciences workflow on multi-core processors and GPUs: a case study for aerosol optical depth retrieval from MODIS satellite data. Int. J. Digital Earth 9, 748–765 (2016)

    Article  Google Scholar 

  5. U. Lohmann, J. Feichter, Global indirect aerosol effects: a review. Atmos. Chem. Phys. 5, 715–737 (2005)

    Article  Google Scholar 

  6. L. Mei, Y. Xue, H. Xu, et al., Validation and analysis of aerosol optical thickness retrieval over land. Int. J. Remote Sens. 33, 781–803 (2012)

    Article  Google Scholar 

  7. NASA, Level 1 and Atmosphere Archive and Distribution System (LAADS Web). Website, 2015. Online at http://ladsweb.nascom.nasa.gov/, visited 20 Oct 2015

  8. U. Pöschl, Atmospheric aerosols: composition, transformation, climate and health effects. Angew. Chem. Int. Ed. 44, 7520–7540 (2005)

    Article  Google Scholar 

  9. W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. (Cambridge University Press, New York, 1992)

    MATH  Google Scholar 

  10. L.A. Remer, R.G. Kleidman, R.C. Levy, et al., Global aerosol climatology from the MODIS satellite sensors. J. Geophys. Res. Atmos. 113, D14S07 (2008)

    Google Scholar 

  11. T.F. Stocker, D. Qin, G.-K. Plattner, et al., Technical summary, in Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, ed. by T.F. Stocker, D. Qin, G.-K. Plattner, et al. (Cambridge University Press, Cambridge/New York, 2013), book section TS, pp. 33–115

    Google Scholar 

  12. J. Tang, Y. Xue, T. Yu, Y. Guan, Aerosol optical thickness determination by exploiting the synergy of TERRA and AQUA MODIS. Remote Sens. Environ. 94, 327–334 (2005)

    Article  Google Scholar 

  13. T.-C. Tsai, Y.-J. Jeng, D.A. Chu, J.-P. Chen, S.-C. Chang, Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008. Atmos. Environ. 45, 4777–4788 (2011)

    Article  Google Scholar 

  14. Y. Wang, Y. Xue, Y. Li, et al., Prior knowledge-supported aerosol optical depth retrieval over land surfaces at 500m spatial resolution with MODIS data. Int. J. Remote Sens. 33, 674–691 (2012)

    Article  Google Scholar 

  15. F. Warmerdam, GDAL - Geospatial Data Abstraction Library. Website, 2015. Online at http://www.gdal.org/, visited 20 Oct 2015

  16. Y. Xue, A.P. Cracknell, Operational bi-angle approach to retrieve the earth surface albedo from AVHRR data in the visible band. Int. J. Remote Sens. 16, 417–429 (1995)

    Article  Google Scholar 

  17. Y. Xue, X. He, H. Xu, et al., China Collection 2.0: the aerosol optical depth dataset from the synergetic retrieval of aerosol properties algorithm. Atmos. Environ. 95, 45–58 (2014)

    Google Scholar 

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Acknowledgements

This work was partially funded by the German Ministry for Education and Research (BMBF) under project grant 01—S13016A within the ITEA2-Project MACH.

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Correspondence to Dustin Feld .

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Feld, D., Garcke, J., Liu, J., Schricker, E., Soddemann, T., Xue, Y. (2017). Energy-Efficiency and Performance Comparison of Aerosol Optical Depth Retrieval on Distributed Embedded SoC Architectures. In: Griebel, M., Schüller, A., Schweitzer, M. (eds) Scientific Computing and Algorithms in Industrial Simulations. Springer, Cham. https://doi.org/10.1007/978-3-319-62458-7_17

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