The UberCloud online marketplace for engineers and scientists to discover, try, and buy compute power on demand, in the cloud. Starting with free experiments in the cloud, including application software, cloud hardware, and expertise. Learning by doing how to use your application in the cloud. info.theubercloud.com/case-studies-and-resources

1. The UberCloud
From Project to Product
From HPC Experiment to HPC Marketplace
From HPC Shop to HPC Shopping Mall
Wolfgang Gentzsch
President,The UberCloud
BurakYenier
CEO,The UberCloud
HPC 2014 , Cetraro, July 7 – 11, 2014

2. The UberCloud
From Project to Product
From HPC Experiment to HPC Marketplace
From HPC Shop to HPC Shopping Mall
Wolfgang Gentzsch
President,The UberCloud
BurakYenier
CEO,The UberCloud
Product innovation and scientific insight require computing
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HPC 2014 , Cetraro, July 7 – 11, 2014

3. Summary: UberCloud Progress
 Traction: 1,500 registered orgs, 72 countries, 155 experiment teams
exploring Computing as a Service
 Visible: 60+ articles; 40+ trade shows; prestigious 2013 HPCwire Readers’
ChoiceAward
 Powerful sponsors: Intel,Autodesk, Bull, IDC,ANSYS; talking to 10 more.
 Powerful participant: 4 ofTop 5 CAE ISVs (total 80+); 100+ sw/hw providers;
hundreds of end-users, 600+ renowned experts
 Compendium I & II: 25 + 17 best case studies from Round 1 – 5 HPC
Experiment sponsored by Intel, over 1,000 downloads
 Hired LindaTreiman (from Bright) to take care of our providers and sponsors
 Container technology and run time environment
 UberCloud Marketplace andAppStore
3

4. Engineers & scientists computing tools:
workstations
3 options to use technical compute power
, servers, and clouds

5. Benefits of HPC in the Cloud
Continue using your workstation for your daily design,
and use Cloud resources with additional benefits:
 An HPC system at your finger tip, on demand
 Pay per use (no CAPital EXpenditure)
 Scaling resources up and down (business flexibility)
 Low risk by working with multiple cloud providers.

6. The challenges
 Workstation: slow, limited capacity
 HPC server: expensive (TCO!), complex
 HPC in the Cloud: security, licensing,
data transfer, expertise, and …
 Very crowded cloud services market,
difficult to find your ideal service

7. It all started June 2012 with the free
voluntary UberCloud Experiments
HPC as a Service, on demand, in a team experiment
For SMBs and their engineering applications
to explore the end-to-end process
of using remote computing resources,
as a service, on demand, at your finger tip,
and learning how to resolve the roadblocks.

8. How does the Experiment work?
 End-User registers
 SoftwareVendor joins
 We select a Team Expert
 Matching a Resource Provider
 152 UberCloud Experiments so far
 42 case studies in Compendium I & II
 Assigning an UberCloud mentor
 Now, the team is ready to go
 Finally, writing the Case Study

9. 22 StepsTowards a successful project
Step 1: define end-user project
 1.1: TE & EU fill out "Project definition" docu
 1.2: UC assigns SP based on "Project definition" docu
 1.3: UC +TM assign RP based on "Project definition" docu
 1.4: TE calls for a kick-off meeting over Skype via Doodle
 1.5: RP fills out "Computing resources" docu
 1.6: SP fills out "Software resources" docu
 1.7: If custom code, EU fills out "Software resources" docu
 1.8 TE +TM review UC Exhibit, consider additional services
EU = end user, SP = software provider, RP = resource provider,TE = team expert,
TM = team mentor, UC UberCloud

10. 22 StepsTowards a successful project
Step 2 & 3: resources & execution
Step 2: Contact the resources, set up the project environment
 2.1: TE gets resources using "Computing resources" docu
 2.2: TE & RP set up software using "Software resources" docu
 2.3: TE & RP set up EU code using "Software resources" docu
 2.4: TE & RP configure project environment
 2.5: TE performs a trial run
Step 3: Initiate project execution on cloud resources
 3.1: TE & EU upload data to the project environment
 3.2: TE & RP queue the job(s) for the project
EU = end user, SP = software provider, RP = resource provider,TE = team expert,
TM = team mentor, UC UberCloud

11. 22 StepsTowards a successful project
Step 4-6: monitor, review, report
Step 4: Monitor the project
 4.1: TE monitors the job status
 4.2: TE & EU re-set parameters between runs as needed
 4.3: TE & RP performs post processing, such as remote viz
Step 5: Review your results
 5.1: TE makes results available to EU, if needed repeats Step 2-5
 5.2: TE & RP remove EU data from project environment
Step 6: Document your findings
 6.1: TE initiates docu "Template for UC Experiment Uses Cases"
 6.2: TE requests team to contribute to and review the docu
EU = end user, SP = software provider, RP = resource provider,TE = team expert,
TM = team mentor, UC UberCloud

12. Step by Step process
Basecamp project management platform for each team

13. The UberCloud HPC Experiments
Started July 2012, 1500 participants, 72 countries
Example: AmazonAWS in the UberCloud:
 Team 2:
 Team 20:
 Team 30:
 Team 40:
 Team 65:
 Team 70:
 Team 116:
 Team 142:
 Team 147:
13
Simulation of a Multi-resonant Antenna System
Turbo-machinery Application Benchmarks
HeatTransfer Use Case
Simulation of Spatial Hearing
Weather Research with WRF
Next Generation Sequencing Data Analysis
Quantitative Finance Historical Data Modeling
VirtualTesting of Severe Service ControlValve
Compressor Map Generation Using Cloud-Based CFD

14. The UberCloud HPC Experiments
Started July 2012, 1500 participants, 72 countries
Example: Bull extreme factory in the UberCloud:
 Team 5:
 Team 8:
 Team 32:
 Team 52:
 Team 85:
 Team 89:
 Team 120:
14
2-phase Flow Simulation of a Separation Column
Flash Dryer with Hot Gas to EvaporateWater from a Solid
2-phase flow simulation of a separation columns
Simulations of Blow-off in Combustion Systems
Combustion simulations of power plant equipment
Simulations of Enzyme-Substrate reactions
Simulation of water flow around self-propelled ship

15. © 2013 ANSYS, Inc. July 16, 201415
Some Lessons Learned
- UberCloud HPC Experiment
Team 8: Flash Dryer Simulation (ANSYS Fluent)
Simulation throughput criterion was met
‼ Remote visualization solution required
‼ Time for downloading results
‼ IP concern
Team 9: Irrigation Simulation (ANSYS CFX)
Timely, high fidelity results were obtained
‼ Windows above Linux preferred
‼ HPC workshop services for SMEs requested
Ability to conduct parametric simulations
‼ Sufficient number of licenses needed
‼ Remote visualization solution required
‼ Disappointing hardware performance results
Team 34: Wind Turbine Simulation (ANSYS Fluent)
Source: The UberCloud HPC Experiment: Compendium of Case Studies

16. © 2013 ANSYS, Inc. July 16, 201416
Some Lessons Learned
- UberCloud HPC Experiment
Team 36: IC-Engine Simulation (ANSYS Fluent)
Smooth setup of environment and sw
‼ Appropriate cloud licensing required
‼ Network bandwidth not good for graphics
‼ Customized sw needs to be recompiled
Team 54: Pool Plant Simulation (ANSYS CFX)
Ability to easily burst into the Cloud
Accelerated file transfer and 3D graphics
‼ Cost of the commercial CFD licenses
Ease of use
Good remote visualization
‼ File uploading time
‼ Stress test with multiple users required
Team 56: Axial Fan Simulation (ANSYS Fluent)
Source: The UberCloud HPC Experiment: Compendium of Case Studies

17. Team 1: Heavy DutyABAQUS
Structural Analysis in the Cloud
TheTeam:
 Frank Ding, is the EngineeringAnalysis and Computing Manager at
Simpson Strong-Tie in Northern California.The end user problem space…..
 Matt Dunbar, is now the ChiefArchitect and CAE technical specialist at
Simulia Dassault Systems, in Rhode Island on the East Coast. He represents
the application level expertise in this experiment.
 Steve Hebert, is one of the founders and CEO of Nimbix, located inTexas,
which in this team is the provider of cloud-based HPCinfrastructure and
applications hosting
 Rob Sherrard, is the other co-founder of Nimbix andVP of Service Delivery.
 Sharan Kalwani, HPC Segment Architect with Intel Corporation and in this
project is the overall Subject Matter Expert,located in Michigan (Midwest).

18. Team 1:The problem to be solved
 The Use Case:
 ABAQUS/Explicit and ABAQUS/Standard are the major applications
 HPC cluster at Simpson Strong-Tie is modest, 32 cores of Intel x86-based gear.
 Cloud bursting is critical.
 Also challenging is the issue of sudden large data transfers
 Need to perform visualization ensuring design simulation is proceeding correctly
 Workflow
 Pre-processing happens on end user’s workstation to prepare the CAE model
 Files transferred to HPC cloud data staging area using a secured FTP process
 Submit the job through (Nimbix.net) web portal
 Result files can be transferred back for post-processing,
 or the post-processing can be done using remote desktop tool like HP RGS on
the HPC provider’s visualization node.

19. Team 1: Challenges!
 A weekly schedule – was not the first challenge!
 Needed a fast interconnect (e.g. Infiniband) which was not available.
 Solved with “fat” nodes, as this cluster is a sandbox for testing the cloud workflow,
the actual inter-connect performance of this 12 core cluster was not a concern.
 The second challenge was to address the need for simple and secure file storage and
transfer. Accomplished very quickly using GLOBUS technology.These days cloud
based storage is mature and ready for prime time HPC, especially in the CAE arena.
 The third challenge was now to push the limits and stream several jobs
simultaneously to the remote HPC cloud resource.This provided solid evidence that
“bursting” was indeed feasible.To the whole team’s surprise it worked admirably
and had no impact whatsoever overall.
 The fourth and final challenge now became perhaps the most critical which was the
end user perception and acceptance of the cloud as a smooth part of the workflow.
 Remote visualization was necessary to see if the simulation results (left remotely in
the cloud)

20. Team 1:What the end user saw…..
 With right tuning, useful remote visualization!

21. Team 1:What did we learn?
 Benefits:
 Clearly established - HPC cloud model can indeed be made to work.
 Recommendations:
 A few key necessary factors emerged:
 Result file transfers: most CAE result files easily over several gigabytes, a
minimum of 2-4 MB/sec sustained and delivered bandwidth is necessary
 The same applies when doing remote visualizations, in this case, 4 MB/sec is
the threshold Latency is also a key concern.
 Beyond the Cloud service provider, a network savvy ISP is perhaps a
necessary part of the team of infrastructure in order to deliver robust and
production like HPC cloud
 Remote visualization provides a convenient collaboration platform for a CAE
analyst to access the analysis results any where he has the need, but it
requires a secure “behind the firewall” remote workspace

22. Team 2: Simulating new probe design
for a medical device
HPC Expert:
End User:
wanted to stay anonymous
Credits
from:

23. Team 70 Case Study: Next Generation
Sequencing DataAnalysis
 MEET TEAM 70:
 End User -Thomas Dyar, Senior Genomics Data Scientist,
Betty Diegel, Senior Software Engineer, medical devices
company
 Software Provider - Brian O'Connor, CEO Nimbus Inform..
Cloud services for workflows utilizing SeqWare
 Resource Provider - AmazonWeb Services
 HPC Cloud Experts - Cycle Computing

24. Team 142 Case Study:Virtual testing of
severe service control valve
 MEET TEAM 142:
 End User – Mark Lobo, Lobo Engineering;
 Software Provider – Derrek Cooper,Autodesk CFD 360
 Resource Provider - AmazonWeb Services
 HPC Cloud Experts – Jon den Hartog
and Heath HoughtonAutodesk

25. Challenges with the experiments
 HPC is complex; at times it requires multiple experts
 Reaching out to industry end-users
 No standards: access and usage of hw & sw
providers are different, some are complex
 Lack of automation: Currently the end-to-end process of the
HPC experiment is manual (intentionally).
 Time delays: vacation, conferences, and
everybody has a day job (busy!)
 Barriers: Complexity, data transfer, security, IP,
software licenses, performance, interoperability…
AND: we learn a lot . . . .

26. Bumps on the road
 Time delays:Vacation times in July/August and December
 No standards: Access and usage processes of
hw & sw providers are different, some complex
 Hands-on: Process automation at providers
vary greatly.
 Lack of automation: Currently the end-to-end process of the
HPC experiment is manual (intentionally).
 Participants spent relatively small portion of their time, some
are responsive, others are not: it is not their day job!
 Getting regular updates fromTeam Experts is a challenge
because this is not their day job !

27. Building a marketplace
demands building an ecosystem
UC
Market
Place
App
store
Comm
unity
Start:
Rough
idea
Mar
Com
ß Pro
duct
Techn
ology
Exper
iment
Exhib
ition
06/12 HPC Cetraro
09/12
01/13
01/13
01/14
01/14
03/14
06/14
workflow impact

28. Problem: today’s crowded
and ineffective cloud ‘market’
Supply
Cloud providers
ISVs
Consultants
Trainers
Demand
Engineers
Scientists
Data analysts
Experts
.
.
.
.
.
Complexity
Data
Transfer
SecurityLicensing
Uncertain
Cost
Roadblocks

29. Solution:
The UberCloud Marketplace
Supply
Cloud providers
ISVs
Consultants
Trainers
…
Demand
Engineers
Scientists
Data analysts
Experts
UberCloud
Marketplace

30. Solution:
The UberCloud Marketplace
UberCloud
Marketplace
for 20+ million engineers and scientists
and their service providers
to discover, try, buy, and sell
computing time, storage, software and expertise
on demand

31. Announcement at HPC Cetraro

32. Technology solution:
StandardCloud run-time environment
 Building thin, light-weight run-time environment (RTE) on
top of Linux kernel features and open source tools, which
 provides a standard platform across distributed in-house,
grid, and cloud resources
 facilitates access to all kinds of resources (workstations,
servers, and private, hybrid, and public clouds)
 moving portable, stackable units including end-users app,
data, tools seamlessly btwn in-house and external resources
 enables portability across different in-house and external
resources (federation)
 reducing / removing many of the cloud challenges
32

33. Builder
Launcher
Controller
ISV DataTools
Stackable units with tools (ex: encryption), ISV application codes (ex: OpenFOAM).
Just add your own codes and data.
Run anywhere with UberCloud Run Time.
Scale up or down the compute power as needed.
Collect granular usage data, logs.
Monitor, alert, report.
Any
Workstation
Any Cluster Any Cloud
Run Time Run Time Run Time
Build once, run anywhere

34. Portable Units are like containers
 Standard software units (with user’s app, data, tools etc.)
can be moved seamlessly across any set of resources.
Units are
 stackable and portable,
 built from a base unit with standard functionality (security,
encryption, compression, monitoring, data transfer, etc)
 extended by the ISV’s software as next layer,
 top layer is the end-users configuration and data.
34

35. Next Steps:
Reducing / Removing Cloud Challenges
Challenge *) Addressed today With UberCloud **)
Portability low high
Security medium high
Software Licenses low medium
DataTransfer low medium
Compliance low medium
Standardization low high
Cost & ROITransparency low high
Resource Availability medium high
Transparency of Market low high
Cloud Computing Expertise low medium
*) Cloud challenges are addressed low, or medium, or high
**)When UberCloud is fully developed one year from now

36. It’s your turn now 
 Download 2013 Compendium of case studies from
HPCwire
 Download 2014 Compendium of case studies
 Register atTheUberCloud.com
 Try the UberCloud Marketplace with $1 voucher
and you get
 NOW NOW

37. The UberCloud Community and Marketplace
ThankYou !
Register free at
http://www.TheUberCloud.com