Clusters, Grids & Clouds for Engineering Design, Simulation, and Collaboration

PARENG Parallel, Distributed & Grid Computing for Engineering
Pecs, April 6-8, 2009

Clusters, Grids & Clouds
for Engineering Design, Simulation, Collaboration
(not only portals…)

Wolfgang Gentzsch
The DEISA Project & The Open Grid Forum

Thanks to
Loren K. Miller, Datametric Innovations, for Goodyear example
Beppe Ugolotti, NICE-Italy, for EnginFrame example

Wolfgang Gentzsch, PARENG 2009

Contents

The Engineering Challenge: Goodyear Example

The Tools: HPC Clusters, Grids and Clouds

Middleware, Services, Applications

Finally: HPC Cluster, Grid, and Cloud Portals

Example: EnginFrame


The Engineering Challenge
Example: Goodyear

Courtesy: Loren K. Miller, President, Datametric Innovations
“The Intersection of Science, Engineering, and IT”
loren.miller@mac.com
+1 330 310 3341


Prototype-Based Design at Goodyear

Since 1898, Goodyear had
developed new products:
design/build prototypes/test
methodology:
– Significant resources were
capitalized and dedicated to
tire building and testing.
– Processes and release
procedures were written
assuming the design/build/test
process.

Design methodology rooted in
building/testing prototypes.

Simulation-Based Engineering Science (SBES)

– “Scope of SBES includes much more than
the modeling of physical phenomena.
• “[SBES] develops new methods, devices,
procedures, processes, and planning
strategies.
• “We hope to solve the most stubborn
problems of modeling, engineering design,
manufacturing, and scientific inquiry.”

– “Modeling and simulation will enable us to
design and manufacture materials and
products on a more scientific basis with less
trial and error and shorter design cycles.”

“Simulation-Based Engineering Science. Revolutionizing Engineering
Science through Simulation.” NSF Blue Ribbon Panel, May, 2006, pp. 3

SBES Vision

1
Road
Tes t
10
Pred i c t i ve
Tes t s

100 0 S imu l a t i on s

Sc i ent i f i c F ounda t i o n

Technical Complexity

Tires are surprisingly complex.
– Geometry.
– Materials.
– Service conditions.

1992: state-of-the-art processes for
creating the models, running the
analyses, and analyzing the results
took months for skilled and
dedicated finite element analysts.

By the time designers got answers,
they’d forgotten their questions.

LR

Technical Complexity: Structures

“The pneumatic tire represents one of the most formidable
challenges in computational mechanics today.”
Professor A. Noor, Journal of Computers and Structures

Modeling Challenges
– Incompressible, non-linear visco-
elastic material with high (~100%)
cyclic strains (rubber)
– Inextensible fibers (steel belts &
polyester ply)
– Flexible structures (sidewall)

~ 60 Million Cycles – Detailed tread patterns
– Wide eigenvalue spectrum
During an 80,000
– Expensive, low fidelity solutions
Mile Tire Lifetime Wolfgang Gentzsch, PARENG 2009

Result: Model Fidelity & Speed

Axisymmetric models. Detailed, treaded models.

Assurance™ TripleTred™ – 2004

The Goodyear Tire & Rubber Company,
First product developed entirely
using simulation-based engineering

Press Photos
science.
Optimized for wet, dry, and ice.
Most successful new product
introduction in Goodyear’s history.


Bottom Line Results

Expenditures on prototype building and testing
dropped 62% (from 40% of the R&D budget to 15%).
– ~$100 million annually that has been directed to other R&D
projects.

Product design times were reduced 67%
(from three years to less than one).
– Key enabler of corporate new product leadership strategy.

Unprecedented string of award-winning new products
resulted from the ability to evaluate many more new
product alternatives.

Results far exceeded what Goodyear dreamed possible in 1992.

Our Tools Today:
HPC Clusters, Grids, and Clouds
and
Middleware, Services, Portals


HPC Clusters
HPC Systems: provide “services“ for the past 30 years
Computing, storage, applications, and data
They serve (local) research, education, and industry (e.g.
HLRS in Stuttgart serving Bosch, Daimler, Porsche)
Very professional: to their end-users, they appear almost
like a set of Cloud services (Amazon definition: easy,
secure, flexible, on demand, pay per use, self serve)
But: no virtualization, semi-automatic, operating in static
mode (increase of performance…)
That’s where HPC centers themselves can become a
Cloud customer, adding dynamic scaling and adopting
to changing business and user demands


Grids

1998: The Grid: Blueprint for a New Computing Infrastructure:
“A computational grid is a hardware and software infrastructure that
provides dependable, consistent, pervasive, and inexpensive
access to high-end computational capabilities.”

2002: The Anatomy of the Grid:
“. . . coordinated resource sharing and problem solving in
dynamic, multi-institutional virtual organizations.”

2002: Grid Checklist:
1) coordinates resources that are not subject to centralized control …
2) … using standard, open, general-purpose protocols and interfaces
3) … to deliver nontrivial qualities of service.

Quotes: Ian Foster, Carl Kesselman, Steve Tuecke


Example: DEISA UNICORE Infrastructure
CINECA user Gateway
Gateway IDRIS Gateway
Gateway FZJ HLRS Gateway job
ECMWF HPCX
Gateway Gateway
job CSC LRZ
NJS
Gateway IDRIS IBM P6 LRZ user
Gateway
CINECA NJS NJS
RZG
FZJ IBM HLRS NEC SX8
IDB UUDB
Gateway Gateway
BSC IDB UUDB IDB UUDB SARA
NJS
ECMWF IBM P5
NJS
AIX
LL-MC HPCX Cray XT4
IDB UUDB
Super-UX
AIX NQS II IDB UUDB
LL-MC P
FT
G rid
AIX
LL
UNICOS/lc
NJS PBS Pro
NJS
CSC Cray XT4/5
LRZ SGI ALTIX

IDB UUDB UNICOS/lc
PBS Pro IDB UUDB
LINUX
PBS Pro

NJS
CINECA IBM P5
AIX
LL-MC
AIX
IDB UUDB LL-MC
LINUX LINUX NJS
Maui/Slurm LL RZG IBM

NJS NJS IDB UUDB
BSC IBM PPC SARA IBM

IDB UUDB IDB UUDB Wolfgang Gentzsch, PARENG 2009

DEISA Service Layers

Multiple Common Presen-
Workflow
ways to production tation
managemnt
access environmnt layer

Co-
Single Job manag.
Job reservation
monitor layer and
rerouting and co-
system monitor.
allocation

Data WAN Data
Data staging
transfer shared manag.
tools
tools File system layer

Network
Unified DEISA Network and
AAA Sites connectivity AAA
layers


DEISA Global File System

IBM P6 & BlueGene/P
IBM P6 & BlueGene/P NEC SX8

AIX, Linux
LL-MC
Super-UX
AIX, Linux NQS II
LL-MC P
FT
IBM P6
G rid Cray XT4
AIX
LL
UNICOS/lc
PBS Pro

Cray XT4/5 UNICOS/lc
PBS Pro SGI ALTIX
LINUX
PBS Pro

AIX
LL-MC
IBM P5 AIX, Linux
LL-MC
LINUX LINUX
Maui/Slurm LL IBM P5+ / P6 IBM P6 & BlueGene/P
IBM PPC

Global transparent file system based on the Multi-Cluster General Parallel File System
(MC-GPFS of IBM)


Clouds

IT resources provisioned outside of corporate data center
Resources accessed over the internet
SaaS, PaaS, IaaS, HaaS
Virtualization: abstraction of the hardware from the service
Build and deliver, always-on, pay-per-use IT services
Near infinite-scale computing, storage, database, related
Web services, AND users
Scaling resources and services up and down
No need on-premises servers and software


Relationship between Grids and Clouds *)

Different main drivers
Grids: sharing resources, collaborating in teams
Clouds: financial and business flexibility, time to market, fast and
low-risk experimentation

Commonalities
Sharing technologies: distributed systems, virtualization
Grid owners are taking advantage of Clouds
Grids and Clouds run on shared infrastructured
Access is via network, often remotely

Similar challenges, major impediments
Portability of applications, services, and data
Secure access to and operation of services
Secure movement and storage of data
Unified management for internal and external platforms

*) OGF Statement on Grids & Clouds, April 2009

A Closer Look at HPC Centers’ Load *. . .

Single, cpu-intensive, tightly-coupled, highly
scalable computational engineering & science
parallel jobs
Single, cpu-intensive, computational, weakly-
scalable, engineering & science parallel jobs
Capacity computing, throughput, parameter jobs

Managing massive data sets, possibly
geographically distributed
Analysis and visualization of data sets

* According to the analysis of T.Sterling and D.Stark, LSU, in a recent HPCwire article


. . . and their Suitability for Clouds

Single, cpu-intensive, tightly-coupled, highly
scalable computational engineering & science No
Not yet
parallel jobs
Single, cpu-intensive, computational, weakly-
scalable, engineering & science parallel jobs Yes
Capacity computing, throughput, parameter jobs Yes
Managing massive data sets, possibly Yes
geographically distributed
Yes
Analysis and visualization of data sets

* According to the analysis of T.Sterling and D.Stark, LSU, in a recent HPCwire article


An HPC Checklist
When is your HPC app ready for the Cloud ?
If there are no issues with licenses, IP, secrecy,
sensitive data, privacy, legal or regulatory issues, . . .
If your app is (almost) architecture independent, not
optimized for specific architecture (i.e. single process,
loosely-coupled low-level parallel, I/O-robust)
If it’s just one app and zillions of parameters
If latency and bandwidth are not an issue
If time (wait, wall, run) doesn’t really matter
If your job is low-priority, simple SLAs, can re-run, . . .

Ideally, your HPC Center’s meta-scheduler knows
all the details, schedules automatically, and
hides all complexity underneath a portal ☺

Finally:
The Cluster, Grid, and Cloud Portal

Example: EnginFrame


Engineering today…

Scripts Aliases
Aliases FTP NFS
Scripts FTP NFS
Engineers enhance the quality of the products Restart
Repository DOE Restart
Repository DOE innovation in the product line
Engineers foster Teamwork
Teamwork
Versioning
Engineers build reusable knowledge for core business
Versioning
LSF
LSF
Library
Library Disk
Disk Windows
So each CRASH! spent by your quota
minute engineers is of great value
quota
Windows
CRASH! Queue
for your company, besides being greatly self-motivating
Queue
Linux
Linux IP
IP Convert
Convert
Protection
Protection Resource
Resource
Working
Working
Password
Password Execution directory
Execution directory
UNIX ID
UNIX ID host
host

Productive Grid and Cloud Solutions

Grid and Cloud Portal

Multi-site Management

Security / Authorization
License Management
ROI Analysis - BI

Flow/Process Management

Workload Management

Data Management

Application Management


What Issues are Addressed

Complex IT infrastructure
– Difficult to optimally leverage resources
– Different programs, applications, GUIs, OS, SAN, SOA
Data management and security
– Timely, consistent, transparent data access
– Controlled access for IP protection
Teamworking and collaboration
– Complex, slow, ad-hoc collaboration
– Identity management
New business opportunities
– ASP, compute-on-demand, HPC consolidation
– Experience sharing and leveraging


Use of Portals
Enterprise Open Grid
Grid ASP

Desktop
Scavenging

l aS
aaaS
rtaal
or t S
C S
. .PPo Commercial HPPC
p H
Appp HPC ASP
C A
PPC
HPC Clusters HH

ign
ssign
e rm
faarm
eedde n f
tiv titoon
raativ a i
boor liz
aaliza
lllaab isu
CCo l
o VVisu

The Grid Portal Gateway

Partners

Grid / Compute Farm

Standard protocols
Managers
Win LX
Batch
Applications
Mac UX Licenses

Internal Users Intranet Clients
Grid Portal
/ Gateway Interactive
Applications

Home Users

Storage and Data
Enterprise
Portal


Benefits for the Engineer

Evolutionary deployment
– Preserve all investments in scripting
– Painless roll-out side-by-side with terminal or remote desktop
– Handles complexity preserving user-friendly approach
Integrated with ISVs and mainstream middleware
– Transparent data management capabilities
– Reduce errors and misuse of the Grid / applications
– Cut training costs and improve users’ productivity
Integrated with engineering workflow engines
– Accelerate supply chain collaboration
– Bottom-up and top-down engineering process automation
– Standardize and enrich data management


Benefits for the IT Manager

Reduced costs
– Menu-based, intuitive, application-centric interface
– Broaden and maximizes the exploitation of the IT infrastructure
– Lower client TCO
Reduced risks
– Evolves with your IT infrastructure and Grid
– Align with company’s IT security policies
– Controlled access to data and information
Exploitation of Server Consolidation/Virtualization
– Black-box, application-level virtualization
– One-stop-shop for computing, visualization, data
– Only one customization for multiple access media / patterns


Portal Services, e.g. EnginFrame

Portlet Client News
Containers Applications Feeds

JSR168 WSDL/SOAP HTTP
Plugins
Skins / Themes Portlet GW WS GW RSS GW
ISV 1 - XML
Template-based dynamic presentation engine with AJAX support
Application Kit
Single-Sign-On
ACL manager Auth. delegation Channel security

ISV n - XML Session manager User mapping Usage acct./billing engine
GUI
Application Kit Service chaining Distributed file manager Virtualization
Custom XML Multi-language services Data life-cycle manager
Workflow
Application Kits App. virtualization GridML virtualization Data virtualization Engine
Compute Grid Data
(Compute Cluster Pack, LSF, PBS, …)

Internal Utility Distributed
HW/SW Services Storage


Interactive job submission

User friendly,
Application-oriented
Job submission

Flexible and efficient
Input file management

Hide complexity of
Underlying scheduler


Monitoring & control

Global Job
monitoring

Cluster & host
monitoring

Job details &
control


Job and service notification


Output management

Data lifecycle
management

Comprehensive output
File manipulation
(view, edit, delete, zip, …)

Follow-up actions
support


License / Job / Queue monitoring


Seamless Interactive Application Integration

VNC, Citrix,
X-Windows


Integration of 3D Preview


Interactive applications (3D)

IBM
DCV


SOA-enabled job submission
WS-I interface
Java / .NET client library and
command line interface
Simplifies integration with
client-side applications
(optimization, workflow, etc.)
for power-users


Enterprise Portal integration


Data exchange, sharing and versioning


Workflow integration

Tools
HTML/HTTP
Extranet Workflow Engine
collaborate
Portal (Process Manager)

r
n ito
o
/ m
it Storage and Data
b m
HTML/HTTP Intranet Su
Portal Grid
EnginFrame
Computational Power


PARENG Parallel, Distributed & Grid Computing for Engineering
Pecs, April 6-8, 2009

Thank You !
And thanks to:

Loren K. Miller, Datametric Innovations, for the Goodyear example
Loren.miller@mac.com

Beppe Ugolotti, NICE-Italy, for the EnginFrame example
Beppe@nice-italy.com


Clusters, Grids & Clouds for Engineering Design, Simulation, and Collaboration

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