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

Wolfgang Gentzsch, PARENG 2009

Clusters, Grids & Clouds

for Engineering Design, Simulation, Collaboration (not only portals…)

Wolfgang GentzschThe DEISA Project & The Open Grid Forum

Thanks to

Loren K. Miller, Datametric Innovations, for Goodyear exampleBeppe Ugolotti, NICE-Italy, for EnginFrame example

PARENG Parallel, Distributed & Grid Computing for Engineering

Pecs, April 6-8, 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”

[email protected]+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

1000 Simu la t i ons

10

Pred i c t i ve

Tes t s

1

Road

Tes t

Sc i ent i f i c Founda 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.


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

LR

Modeling Challenges– Incompressible, non-linear visco-

elastic material with high (~100%)

cyclic strains (rubber)

– Inextensible fibers (steel belts &

polyester ply)

– Flexible structures (sidewall)

– Detailed tread patterns

– Wide eigenvalue spectrum

– Expensive, low fidelity solutions

~ 60 Million Cycles

During an 80,000

Mile Tire Lifetime


Result: Model Fidelity & Speed

Axisymmetric models. Detailed, treaded models.


Assurance™ TripleTred™ – 2004

� First product developed entirely using simulation-based engineering science.

� Optimized for wet, dry, and ice.

� Most successful new product introduction in Goodyear’s history.

The G

ood

year T

ire & R

ubber C

om

pan

y,

Press P

hoto

s


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 adoptingto 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 interfaces3) … to deliver nontrivial qualities of service.

Quotes: Ian Foster, Carl Kesselman, Steve Tuecke


Gateway

CSC

Gateway

ECMWF

Gateway

FZJ

Gateway

IDRIS

Gateway

SARA

Gateway

LRZ

Gateway

HPCX

Gateway

HLRS

NJS CINECA IBM P5

IDB UUDB

Gateway

BSC

Gateway

CINECA NJS FZJ IBM

IDB UUDB

NJS RZG IBM

IDB UUDB

NJS ECMWF IBM P5

IDB UUDB

NJS CSC Cray XT4/5

IDB UUDB

NJS HPCX Cray XT4

IDB UUDB

NJS LRZ SGI ALTIX

IDB UUDB

NJS HLRS NEC SX8

IDB UUDB

CINECA user

LRZ user

job

job

NJS SARA IBM

IDB UUDB

NJS BSC IBM PPC

IDB UUDB

Gateway

RZG

NJSIDRIS IBM P6

IDB UUDB

AIXLL-MC

AIXLL

LINUXPBS Pro

Super-UXNQS II

GridFTP

LINUXMaui/Slurm

UNICOS/lcPBS Pro

LINUXLL

AIXLL-MC

AIXLL-MC

UNICOS/lcPBS Pro

AIXLL-MC

Example: DEISA UNICORE Infrastructure


DEISA

Sites

UnifiedUnified

AAAAAANetworkNetwork

connectivityconnectivity

DataData

transfer transfer

toolstools

Data stagingData staging

toolstools

JobJob

reroutingrerouting

SingleSingle

monitormonitor

systemsystem

CoCo--

reservationreservation

and coand co--

allocationallocation

WorkflowWorkflow

managemntmanagemnt

MultipleMultiple

ways toways to

accessaccess

CommonCommon

productionproduction

environmntenvironmnt

WANWAN

sharedshared

File systemFile system

Network

and

AAA

layers

Job manag.

layer and

monitor.

Presen-

tation

layer

Data

manag.

layer

DEISA Service Layers


AIXLL-MC

AIXLL

LINUXPBS Pro

Super-UXNQS II

GridFTP

UNICOS/lcPBS Pro

LINUXLL

AIX, LinuxLL-MC

AIX, LinuxLL-MC

IBM P5

IBM P6 & BlueGene/P

IBM P6 & BlueGene/P

IBM P6

Cray XT4/5

Cray XT4

SGI ALTIX

NEC SX8

IBM P5+ / P6IBM PPC

IBM P6 & BlueGene/P

UNICOS/lcPBS Pro

AIX, LinuxLL-MC

DEISA Global File System

LINUXMaui/Slurm

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 *)

� Grids: sharing resources, collaborating in teams� Clouds: financial and business flexibility, time to market, fast and

low-risk experimentation

� 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

� 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

Different main drivers

Commonalities

Similar challenges, major impediments

*) 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 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

No

Yes

Yes

Yes

Yes

Not yet


An HPC ChecklistWhen 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…

� Engineers enhance the quality of the products

� Engineers foster innovation in the product line

� Engineers build reusable knowledge for core business

� So each minute spent by your engineers is of great valuefor your company, besides being greatly self-motivating

LSFLSF

LibraryLibrary

Working directory

Working directory

ScriptsScripts

QueueQueue

Executionhost

Executionhost

NFSNFS

PasswordPassword

AliasesAliases

ResourceResource

FTPFTP

UNIX IDUNIX ID

LinuxLinux

RepositoryRepository RestartRestart

WindowsWindowsDisk quota

Disk quota

VersioningVersioning

DOEDOE

IPProtection

IPProtection

TeamworkTeamwork

ConvertConvert

CRASH!CRASH!


Productive Grid and Cloud Solutions

Application Management

Data Management

Workload Management

License Management

Multi-site Management

Grid and Cloud Portal

Secu

rity/ A

uth

orizatio

n

RO

I Analy

sis-

BI

Flow/Process 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

Collabora

tive design

Collabora

tive design

Visualizatio

n farm

Visualizatio

n farm

HPCSaaS

HPCSaaS

HPCApp. Porta

l

HPCApp. Porta

l

Open Grid

ASP

Commercial

HPC ASP

HPC Clusters

Enterprise

Grid

Desktop

Scavenging


The Grid Portal Gateway

InteractiveApplications

Intranet Clients

Win LX

UXMac

Grid / Compute Farm

Internal Users

BatchApplications

Storage and Data

Grid Portal/ Gateway

Home Users

Managers

Partners

Sta

ndard

pro

tocols

Licenses

EnterprisePortal


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


Data

DistributedStorage

Compute Grid(Compute Cluster Pack, LSF, PBS, …)

InternalHW/SW

UtilityServices

WSDL/SOAP

Client Applications

HTTP

NewsFeeds

JSR168

PortletContainers

Skins / Themes

Auth. delegation

Custom XMLApplication Kits

ISV n - XMLApplication Kit

ISV 1 - XMLApplication Kit

WorkflowEngine

GUIVirtualization

Single-Sign-On

Portal Services, e.g. EnginFrame

Template-based dynamic presentation engine with AJAX support

Portlet GW WS GW RSS GWPlugins

ACL manager

User mapping

App. virtualization GridML virtualization Data virtualization

Usage acct./billing engineSession manager

Channel security

Service chaining

Multi-language services Data life-cycle manager

Distributed file manager


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

Computational Power

Storage and Data

Grid

EnginFrame

Intranet

Portal

Extranet

Portal

HTML/HTTP Subm

it/ m

onitor

Workflow Engine

(Process Manager)

HTML/HTTP

collaborate


Thank You !

And thanks to:

Loren K. Miller, Datametric Innovations, for the Goodyear [email protected]

Beppe Ugolotti, NICE-Italy, for the EnginFrame [email protected]

PARENG Parallel, Distributed & Grid Computing for Engineering

Pecs, April 6-8, 2009

Technology

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