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Multi-Channel Radar Depth Sounder (MCRDS) signal processing: A distributed computing approach

Multi-Channel Radar Depth Sounder (MCRDS) signal processing: A distributed computing approach. Research Questions. Does the addition of computing cores increase the performance of the CReSIS Synthetic Aperture Radar Processor (CSARP)?

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Multi-Channel Radar Depth Sounder (MCRDS) signal processing: A distributed computing approach

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  1. Multi-Channel Radar Depth Sounder (MCRDS) signal processing: A distributed computing approach

  2. Research Questions • Does the addition of computing cores increase the performance of the CReSIS Synthetic Aperture Radar Processor (CSARP)? • What MATLAB toolkits and/or expansion kits are necessary to run CSARP ? • What hardware requirements are necessary to store and process CReSIS collected data? • What facility environmental requirements are there to house a cluster of at least 32 cores to process a data set? • What is the process to prepare a cluster from a middle-ware stand-point? • Can an open-source job scheduler replace the MATLAB proprietary Distributed Computing Server currently required by CSARP?

  3. Hypothesis It was believed that the addition of computing cores would increase the performance of CSARP run times within a 10% level of significance. More nodes = Lower run times

  4. CSARP Function Ice Sheet Imagery Data File

  5. SAR and MCRDS Relation Synthetic Aperture Radar Multi-Channel RADAR Depth Sounder Provided by: Radartutorial.eu Greenland 2008 Deployment

  6. Distributed Computing ADMI Cluster Testing – 1 Node

  7. Distributed Computing ADMI Cluster Testing – 2 Nodes

  8. Distributed Computing ADMI Cluster Testing – 4 Nodes

  9. Distributed Computing ADMI Cluster Testing – 8 Nodes

  10. Distributed Computing ADMI Cluster Testing – Results

  11. Grid verses Cluster Topography

  12. Cluster Setup (Madogo)

  13. Power and Cooling Consumption Comparison Average Home ~3 Tons 2.75 Tons

  14. Madogo Cluster

  15. Middleware

  16. Data Collection

  17. Results Statistical Hypothesis and Test Value Analysis of Variance (ANOVA) Madogo Worker Mean Times (minutes) Collected Data ANOVA Testing P-value < α therefore we must reject H0 Analysis and Decision

  18. Results 67% Increase There is significant evidence to indicate there is a difference in the performance times of CSARP with the inclusion of additional workers with a 10% level of significance.

  19. Futurework and Recommendations 128 Node Estimation Point at which overhead outweighs distribution benefits 128 Nodes 32 Nodes

  20. Questions Contact Information: Je’aime H. Powell Jeaime.powell@cerser.ecsu.edu Web Site: http://Cerser.ecsu.edu

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