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)?

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?

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

CSARP FUNCTION

Data File

Ice Sheet Imagery

SAR AND MCRDS RELATION

Provided by: Radartutorial.eu

Synthetic Aperture Radar Multi-Channel RADAR Depth Sounder

Greenland 2008 Deployment

DISTRIBUTED COMPUTING

ADMI Cluster Testing – 1 Node

DISTRIBUTED COMPUTINGADMI Cluster Testing – 2 Nodes

DISTRIBUTED COMPUTINGADMI Cluster Testing – 4 Nodes

DISTRIBUTED COMPUTINGADMI Cluster Testing – 8 Nodes

DISTRIBUTED COMPUTING

ADMI Cluster Testing – Results

1 Node 2 Nodes 4 Nodes 8 Nodes0

2

4

6

8

10

12

10 10 10 10

Human Node Performance

Number of Human Nodes

Run

Tim

e in

Sec

onds

GRID VERSES CLUSTER TOPOGRAPHY

CLUSTER SETUP (MADOGO)

POWER AND COOLING CONSUMPTION COMPARISON

Average Home

~3 Tons 2.75 Tons

MADOGO CLUSTER

MIDDLEWARE

DATA COLLECTION

RESULTS

H 0 :μ1 =μ2 =μ4 =μ8 =μ16 =μ32

H1 : μ1 ≠μ2 ≠μ4 ≠μ8 ≠μ16 ≠μ32

α =.1

1 Worker 2 Workers 4 Workers 8 Workers 16 Workers 32 Workers29.27889049 16.92939551 13.06592702 11.45293885 11.34383124 11.30514759

Madogo Worker Mean Times (minutes)

P-value < α therefore we must reject H0

Statistical Hypothesis and Test Value

Collected Data

ANOVA Testing

Analysis and Decision

Analysis of Variance(ANOVA)

RESULTS

1 Worker 2 Workers 4 Workers 8 Workers 16 Workers 32 Workers0

200400600800

100012001400160018002000

Madogo Worker Mean Run Times

Number of Workers

Run-

time

Mea

ns in

Sec

onds

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.

67% Increase

FUTUREWORK AND RECOMMENDATIONS

128 Node Estimation

32 Nodes

128 Nodes

Point at which overhead outweighs distribution

benefits

QUESTIONS

Contact Information:

Je’aime H. PowellJeaime.powell@cerser.ecsu.edu

Web Site:

http://Cerser.ecsu.edu