NASA Goddard: Head in the Clouds

AWS Government, Education, and Nonprofit Symposium
Washington, DC I June 25-26, 2015
NASA Goddard:
Head in the Clouds
Dan Duffy, NASA
Steve Orrin, Intel
Tim Carroll, Cycle Computing
©2015, Amazon Web Services, Inc. or its affiliates. All rights reserved.

Fastest growing workloads
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The problem in 2013:
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The options they didn’t choose:
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• Write software for 9–12 months this 1 app
Solution:
• Created 10,600 server cluster
• 39.5 years of computing in 8 hours
• Found 3 potential drug candidates!
• Total infrastructure bill: $4,372
6

Cycle powers cloud BigData and BigCompute
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Analytics
Modeling
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Compute Burst
Software required to drive
analytics and simulation at
scale:
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• On-demand
• Ask the right questions

Best way to try it… try it
8
to try it…try Tim@cyclecomputing.com

Measure Woody Biomass on South Side of the
Sahara at the 40–50 cm Scale Using AWS
Overview of the NASA Head in the Clouds Project presented at the Amazon Web Services
Public Summit 2015
Daniel Duffy daniel.q.duffy@nasa.gov and on Twitter @dqduffy
High Performance Computing Lead at the
NASA Center for Climate Simulation (NCCS) – http://www.nccs.nasa.gov and @NASA_NCCS
Goddard Space Flight Center (GSFC) – http://www.nasa.gov/centers/goddard/home/

ESD Project Won Intel Head in Clouds Challenge Award to
Estimate Biomass in South Sahara
Project Goal
• Using NGA data to estimate tree and bush biomass over the
entire arid and semi-arid zone on the south side of the Sahara
Project Summary
• Estimate carbon stored in trees and bushes in arid and semi-
arid south Sahara
• Establish carbon baseline for later research on expected CO2
uptake on the south side of the Sahara
Principal Investigators
• Dr. Compton J. Tucker, NASA Goddard Space Flight Center
• Dr. Paul Morin, University of Minnesota
Tree
Crown
Shadow
NGA 40 cm imagery representing tree
and shrub automated recognition

Intel
• Professional Services and Funding for AWS Resources
Amazon Web Services (AWS)
• Compute and storage
• Support to set up environment
Cycle Computing
• Cloud Resource Management Software
• Services to install and configure the software
Climate Model Data Services (CDS – GSFC Code 600)
• NGA data support
NASA Center for Climate Simulation (NCCS – GSFC Code 606.2)
• System administration, application support, and data movement
NASA CIO
• General cloud consulting and coordination support
Partners and Resources

Existing Sub-Saharan Arid and Semi-Arid Sub-Meter Commercial Imagery
9600 Strips (~80TB) to Be Delivered to GSFC
~1600 strips (~20TB) at GSFC
Area Of Interest (AOI) for Sub-Saharan Arid and Semi-Arid Africa

The DigtalGlobe Constellation
The Entire Archive is Licensed to the USG
Geoeye
Quickbird
Ikonos
Worldview 1
Worldview 2
Worldview 3 (Available Q1 2015)

14
Panchromatic and multispectral mapping
at the 40- and 50-cm scale

Use Niger as the test case
NGA data over Niger
• Currently have about 16,000 total scenes covering Niger (the data is already orthorectified)
• For this test case, approximately 3,120 scenes need to be processed to generate the vegetation index
• Each scene is approximately 30,000 x 30,000 data points (pixels)
• Will break each scene up into 100 tiles (3,000 x 3,000)
Where is the data?
• Data currently resides within the NCCS and in AWS
Additional data
• If we are successful and have additional time and resources, other African areas can be studied.
15NASA Head in the Clouds Project

Processing requirements
Based on the tests run in the NCCS private cloud, following processing requirements were
estimated
• The tests were run on a single core (Intel E5-2670 2.5 GHz processor) virtual machine with 2 GB of
memory
• Each of the 3,120 scenes is broken up into 100 tiles
• Each tile took 24 minutes
• Hence, one scene will then take 24 * 100 = 2,400 minutes of total processor time (about 40 wall
clock hours)
• Tiles and scenes can be run in parallel
• Total scene to process = 312,000
• Total compute hours = 124,800
Target completion time
• 1 month will take between 175 to 200 virtual machines running non-stop

Input and output data
Input data
• Total input of about 8 TB for the 3,120 scenes
• Average of about 2.63 GB of data per scene
• Average of about 26.3 MB of data per tile
Intermediate data products
• Unsure of how much intermediate data products are needed; this will impact the amount of
temporary space required for each run
Output data products
• Total output data is estimated to be 25% of the input data
• Estimated total output is about 2 to 3 TB
• Output data will be transferred back to the NCCS

Cluster configuration requirements
18
Category Description Requirement
Number of Cores How many cores are required on a single node for the
application?
1 per tile
Amount of Memory (RAM) How much memory on a node (or per core) is required for
the application?
2 GB per tile
Operating System (OS) What operating system does the application need? Linux (Centos or debian)
Libraries/Tools/Software What additional libraries, tools, and software are needed to
be installed? Compilers? Commercial software?
None
Parallelization Can the application run in a parallel manner? If so, how
(threaded, MPI, or multiple instances of the application)?
Inherently parallel processing of each scene
and/or tile
Cluster If the application runs in parallel across many nodes, how
many nodes are required?
175 – 200 to complete in 1 month; more can be
used
Storage How much storage space will be required for each run
(input, intermediate, and output files)?
Total Input – 8 TB (approx. 2.6 GB for each
scene)
Intermediate – To be determined
Total Output Back to NCCS – 2 TB ( approx.
25% of total input)
Shared Storage Does this storage have to be shared across all nodes? No

Workflow
19
DataMan
Cycle Computing Data
Transfer Software
NCCS Science Cloud
(Internal Cloud)
Shared File System
NGA Data at NASA
NGA Data External to NASA (PGC, Digital Globe)
Data to be copied into the
NCCS science cloud NGA
data repository.
NCCS/NASA VM
Local
Data
A resource manager (batch queue) will be running in AWS. Scientists
will interact and launch jobs through the Cycle Computing system
directly in AWS.
Virtual machines will be launched in AWS. After the job is completed,
the results will be copied back to the NCCS.
VM
Local
Data
VM
Local
Data
VM
Local
Data
AWS
VM VM VM
Virtual machines in the internal cloud can read the data directly
from the shared disk in the NASA internal cloud. No additional
data movement is required.
Amazon S3
Data to be processed is staged into Amazon S3. Data will be moved
to the local storage of the VM’s for processing. Products could be
stored in S3 for transfer to the NCCS at a later time.
Batch
Queue
System
The Cycle Computing
DataMan software will
be used to transfer the
data into Amazon S3.
Cycle
Computing
System

Time line
20
Category Dec Jan Feb Mar Apr May Jun Jul Aug Sep
Bi-Weekly Tag Ups
Requirements/Scope
Setup/Configuration
Test Runs
Transfer Data to S3
Configure S3 Buckets
Production Runs
Analysis
Final Report

Why use Cycle Computing and AWS?
• The bigger goal is to analyze the entire arid and semi-arid zone on the south side of
the Sahara
– About 80 TB
– 10x the data that the initial project will analyze
• On 200 virtual machines, this will take 10 months!
– How can we accelerate this?
• Can easily scale up the number of virtual machines using the Cycle Computing
software and the AWS resources
– Once the data is in AWS, 80 TB of data can be analyzed in approximately the same amount
of time as 8 TB of data
– Scientists really love this part!
• Might need longer given the data transfers may take time – can overlap data transfers
and computation
21

Thanks goes to the following…
NASA
• Dr. Compton Tucker (Co-PI)
• Katherine Melocik (GSFC)
• Jennifer Small (GSFC)
• Dr. Tsengdar Lee (HQ)
• Daniel Duffy (GSFC)
• Mark McInerney (GSFC)
• Hoot Thompson (GSFC)
• Garrison Vaughn (GSFC)
• Brittany Wills (GSFC)
• Scott Sinno (GSFC)
• Ray Obrien (ARC)
• Richard Schroeder (ARC)
• Milton Checchi (ARC)
University Partners
• Paul Morin (Co-PI, Univ. Minnesota)
• Claire Porter (Univ. Minnesota)
• Jamon Van Den Hoek (Oak Ridge)
22
Cycle Computing
• Tim Carroll
• Michael Requa
• Carl Chesal
• Bob Nordlund
• Glen Otero
• Rob Futrick
AWS
• Jamie Baker
• Jeff Layton
There are others… My apologies for those I
missed. These are typically the ones on the our
conference calls!

Thank You.
This presentation will be loaded to SlideShare the week following the Symposium.
http://www.slideshare.net/AmazonWebServices

NASA Goddard: Head in the Clouds

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