11

Advances and Changes in Simulation

Geoffrey FoxProfessor of Computer Science, Informatics, Physics

Pervasive Technology Laboratories

Indiana University Bloomington IN 47401

January 20 2004

gcf@indiana.edu

http://www.infomall.org

http://www.grid2002.org

22

Trends in Simulation Research 1990-2000 the HPCC High Performance Computing

and Communication Initiative• Established Parallel Computing• Developed wonderful algorithms – especially in partial

differential equation and particle dynamics areas• Almost no useful software except for MPI – messaging

between parallel computer nodes 1995-now Internet explosion and development of Web

Service distributed system model• Replaces CORBA, Java RMI, HLA, COM etc.

2000- now: almost no academic work in core simulation• Major projects like ASCI (DoE) and HPCMO (DoD) thrive

2003-? Data Deluge apparent and Grid links Internet and HPCC with focus on data-simulation integration

33

Some Implications of Trends New requirements corresponding to Grid/e-Science

technology• Managing distributed data• Integration of data with simulations

Internet (Web Service) software gives better infrastructure for building simulation environments for both event driven and time stepped cases• Build Problem Solving Environments in terms of Web

Services for capabilities like Generate Mesh or Visualize• Adopt Web Service Workflow model for computing with

“Rule of Millisecond”• No new ideas for core parallel computing – just better

software infrastructure and some new applications Data assimilation needs new algorithms and architectures –

Queen Bee Architecture

44

e-Business e-Science and the Grid e-Business captures an emerging view of corporations as

dynamic virtual organizations linking employees, customers and stakeholders across the world.

e-Science is the similar vision for scientific research with international participation in large accelerators, satellites or distributed gene analyses.

The Grid or CyberInfrastructure integrates the best of the Web, Agents, traditional enterprise software, high performance computing and Peer-to-peer systems to provide the information technology e-infrastructure for e-moreorlessanything.

A deluge of data of unprecedented and inevitable size must be managed and understood.

People, computers, data and instruments must be linked. On demand assignment of experts, computers, networks and

storage resources must be supported

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IMAGING INSTRUMENTS

COMPUTATIONALRESOURCES

LARGE-SCALE DATABASES

DATA ACQUISITION ,ANALYSIS

ADVANCEDVISUALIZATION

55

Some Important Styles of Grids Computational Grids were origin of concepts and link

computers across the globe – high latency stops this from being used as parallel machine

Knowledge and Information Grids link sensors and information repositories as in Virtual Observatories or BioInformatics

• More detail on next slide Collaborative Grids link multidisciplinary researchers across

laboratories and universities Community Grids focus on Grids involving large numbers of

peers rather than focusing on linking major resources – links Grid and Peer-to-peer network concepts

Semantic Grid links Grid, and AI community with Semantic web (ontology/meta-data enriched resources) and Agent concepts

Grid Service Farms supply services-on-demand as in collaboration, GIS support, filter

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Information/Knowledge Grids Distributed (10’s to 1000’s) of data sources (instruments,

file systems, curated databases …) Data Deluge: 1 (now) to 100’s petabytes/year (2012)

• Moore’s law for Sensors Possible filters assigned dynamically (on-demand)

• Run image processing algorithm on telescope image• Run Gene sequencing algorithm on compiled data

Needs decision support front end with “what-if” simulations

Metadata (provenance) critical to annotate data

Integrate across experiments as in multi-wavelength astronomy

Data Deluge comes from pixels/year available

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Virtual Observatory Astronomy GridIntegrate Experiments

Radio Far-Infrared Visible

Visible + X-ray

Dust Map

Galaxy Density Map

88

e-Business and (Virtual) Organizations Enterprise Grid supports information system for an

organization; includes “university computer center”, “(digital) library”, sales, marketing, manufacturing …

Outsourcing Grid links different parts of an enterprise together Manufacturing plants with designers• Animators with electronic game or film designers and

producers• Coaches with aspiring players (e-NCAA or e-NFL etc.)• Outsourcing will become easier ……..

Customer Grid links businesses and their customers as in many web sites such as amazon.com

e-Multimedia can use secure peer-to-peer Grids to link creators, distributors and consumers of digital music, games and films respecting rights

Distance education Grid links teacher at one place, students all over the place, mentors and graders; shared curriculum, homework, live classes …

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In flight data

Airline

Maintenance Centre

Ground Station

Global NetworkSuch as SITA

Internet, e-mail, pager

Engine Health (Data) Center

DAME

Rolls Royce and UK e-Science ProgramDistributed Aircraft Maintenance Environment

~ Gigabyte per aircraft perEngine per transatlantic flight

~5000 engines

1010

NASA Aerospace Engineering Grid

•Lift Capabilities•Drag Capabilities•Responsiveness

•Deflection capabilities•Responsiveness

•Thrust performance•Reverse Thrust performance•Responsiveness•Fuel Consumption

•Braking performance•Steering capabilities•Traction•Dampening capabilities

Crew Capabilities- accuracy- perception- stamina- re-action times- SOP’s

Engine Models

Airframe Models

Wing Models

Landing Gear Models

Stabilizer Models

Human Models

Whole system simulations are produced by couplingall of the sub-system simulations

It takes a distributed virtual organization to design, simulate and build a complex system like an aircraft

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e-Defense and e-Crisis Grids support Command and Control and provide Global

Situational Awareness • Link commanders and frontline troops to themselves and to archival and

real-time data; link to what-if simulations

• Dynamic heterogeneous wired and wireless networks

• Security and fault tolerance essential

System of Systems; Grid of Grids• The command and information infrastructure of each ship is a Grid; each

fleet is linked together by a Grid; the President is informed by and informs the national defense Grid

• Grids must be heterogeneous and federated

Crisis Management and Response enabled by a Grid linking sensors, disaster managers, and first responders with decision support

Define and Build DoD relevant Services – Collaboration, Sensors, GIS, Database etc.

12

Database Database

Closely Coupled Compute Nodes

Analysis and Visualization

RepositoriesFederated Databases

Sensor Nets Streaming Data

Loosely Coupled Filters

SERVOGrid for e-Geoscience

?DiscoveryServices

SERVOGrid – Solid Earth Research Virtual Observatory will link Australia, Japan, USA ……

1313

SERVOGrid Requirements Seamless Access to Data repositories and large scale

computers Integration of multiple data sources including sensors,

databases, file systems with analysis system• Including filtered OGSA-DAI (Grid database access)

Rich meta-data generation and access with SERVOGrid specific Schema extending openGIS (Geography as a Web service) standards and using Semantic Grid

Portals with component model for user interfaces and web control of all capabilities

Collaboration to support world-wide work Basic Grid tools: workflow and notification NOT metacomputing

14

Large Scale Parallel Computers

Old Style Metacomputing GridQuickTime™ and a

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IMAGING INSTRUMENTS

COMPUTATIONALRESOURCES

LARGE-SCALE DATABASES

DATA ACQUISITION ,ANALYSIS

ADVANCEDVISUALIZATION

Analysis and Visualization

Spread a single large Problem over multiple supercomputers

Large Disks

1515

Classes of Computing Grid Applications Running “Pleasing Parallel Jobs” as in United Devices,

Entropia (Desktop Grid) “cycle stealing systems” Can be managed (“inside” the enterprise as in Condor)

or more informal (as in SETI@Home) Computing-on-demand in Industry where jobs spawned

are perhaps very large (SAP, Oracle …) Support distributed file systems as in Legion (Avaki),

Globus with (web-enhanced) UNIX programming paradigm• Particle Physics will run some 30,000 simultaneous jobs this

way Pipelined applications linking data/instruments,

compute, visualization Seamless Access where Grid portals allow one to choose

one of multiple resources with a common interfaces

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When is a High Performance Computer? We might wish to consider three classes of multi-node computers 1) Classic MPP with microsecond latency and scalable internode

bandwidth (tcomm/tcalc ~ 10 or so) 2) Classic Cluster which can vary from configurations like 1) to 3)

but typically have millisecond latency and modest bandwidth 3) Classic Grid or distributed systems of computers around the

network• Latencies of inter-node communication – 100’s of milliseconds

but can have good bandwidth All have same peak CPU performance but synchronization costs

increase as one goes from 1) to 3) Cost of system (dollars per gigaflop) decreases by factors of 2 at

each step from 1) to 2) to 3) One should NOT use classic MPP if class 2) or 3) suffices unless

some security or data issues dominates over cost-performance One should not use a Grid as a true parallel computer – it can

link parallel computers together for convenient access etc.

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What is Happening? Grid ideas are being developed in (at least) two communities

• Web Service – W3C, OASIS• Grid Forum (High Performance Computing, e-Science)• Open Middleware Infrastructure Institute OMII currently only in

UK but maybe spreads to EU and USA Service Standards are being debated Grid Operational Infrastructure is being deployed Grid Architecture and core software being developed Particular System Services are being developed “centrally” – OGSA

framework for this in Lots of fields are setting domain specific standards and building

domain specific services Grids are viewed differently in different areas

• Largely “computing-on-demand” in industry (IBM, Oracle, HP, Sun)

• Largely distributed collaboratories in academia

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A typical Web Service In principle, services can be in any language (Fortran .. Java ..

Perl .. Python) and the interfaces can be method calls, Java RMI Messages, CGI Web invocations, totally compiled away (inlining)

The simplest implementations involve XML messages (SOAP) and programs written in net friendly languages like Java and Python

PaymentCredit Card

WarehouseShippingcontrol

WSDL interfaces

WSDL interfaces

Security CatalogPortalService

Web Services

Web Services

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Services and Distributed Objects A web service is a computer program running on either the local

or remote machine with a set of well defined interfaces (ports) specified in XML (WSDL)

Web Services (WS) have many similarities with Distributed Object (DO) technology but there are some (important) technical and religious points (not easy to distinguish)• CORBA Java COM are typical DO technologies• Agents are typically SOA (Service Oriented Architecture)

Both involve distributed entities but Web Services are more loosely coupled• WS interact with messages; DO with RPC (Remote Procedure Call)• DO have “factories”; WS manage instances internally and interaction-

specific state not exposed and hence need not be managed• DO have explicit state (statefull services); WS use context in the messages

to link interactions (statefull interactions) Claim: DO’s do NOT scale; WS build on experience (with

CORBA) and do scale

2020

Technical Activities of Note Look at different styles of Grids such as Autonomic (Robust

Reliable Resilient) New Grid architectures hard due to investment required Critical Services Such as

• Security – build message based not connection based• Notification – event services• Metadata – Use Semantic Web, provenance• Databases and repositories – instruments, sensors• Computing – Submit job, scheduling, distributed file

systems• Visualization, Computational Steering• Fabric and Service Management• Network performance

Program the Grid – Workflow Access the Grid – Portals, Grid Computing Environments

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System and Application Services? There are generic Grid system services: security, collaboration,

persistent storage, universal access• OGSA (Open Grid Service Architecture) is implementing these

as extended Web Services An Application Web Service is a capability used either by another

service or by a user• It has input and output ports – data is from sensors or other

services Consider Satellite-based Sensor Operations as a Web Service

• Satellite management (with a web front end)• Each tracking station is a service• Image Processing is a pipeline of filters – which can be

grouped into different services• Data storage is an important system service• Big services built hierarchically from “basic” services

Portals are the user (web browser) interfaces to Web services

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Satellite Science Grid Environment

Sensor Data as a Web

service (WS)

Data Analysis WS

Sensor Management

WS

Visualization WS

Simulation WS

Filter1WS

Filter2WS

Filter3WS

Build as multiple Filter Web Services

Prog1WS

Prog2WS

Build as multiple interdisciplinaryPrograms

2323

Issues and Types of Grid Services 1) Types of Grid

• R3• Lightweight• P2P• Federation and Interoperability

2) Core Infrastructure and Hosting Environment

• Service Management• Component Model• Service wrapper/Invocation • Messaging

3) Security Services• Certificate Authority• Authentication• Authorization• Policy

4) Workflow Services and Programming Model

• Enactment Engines (Runtime)• Languages and Programming• Compiler• Composition/Development

5) Notification Services 6) Metadata and Information Services

• Basic including Registry• Semantically rich Services and meta-

data• Information Aggregation (events)• Provenance

7) Information Grid Services• OGSA-DAI/DAIT• Integration with compute resources• P2P and database models

8) Compute/File Grid Services• Job Submission• Job Planning Scheduling

Management• Access to Remote Files, Storage and

Computers• Replica (cache) Management• Virtual Data• Parallel Computing

9) Other services including• Grid Shell• Accounting• Fabric Management• Visualization Data-mining and

Computational Steering• Collaboration

10) Portals and Problem Solving Environments

11) Network Services• Performance• Reservation• Operations

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Grid Services for the Education Process “Learning Object” XML standards already exist WebCT Blackboard etc. could be converted to Service Model Synchronous Collaboration Tools including Audio/Video

Conferencing natural Grid Services as in http://globalmmcs.org

Registration Homework submission and Performance (grading) Authoring of Curriculum Online laboratories for real and virtual instruments Quizzes of various types (multiple choice, random parameters) Assessment data access and analysis Scheduling of courses and mentoring sessions Asynchronous access, data-mining and knowledge discovery Learning Plan agents to guide students and teachers

2525

Database Database

Coarse grain simulations

Analysis and Visualization

RepositoriesFederated Databases

Field Trip Data Streaming Data

Loosely Coupled Filters

Sensors

?DiscoveryServices

SERVOGrid for e-Education

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(i)SERVO Web (Grid) Services for PSE• Programs: All applications wrapped using proxy strategy as Services• Job Submission: supports remote batch and shell invocations

– Used to execute simulation codes (VC suite, GeoFEST, etc.), mesh generation (Akira/Apollo) and visualization packages (RIVA, GMT).

• File management:– Uploading, downloading, backend crossloading (i.e. move files between remote

servers) – Remote copies, renames, etc.

• Job monitoring• Workflow: Apache Ant-based remote service orchestration

– For coupling related sequences of remote actions, such as RIVA movie generation.

• Database services: support SQL queries• Data services: support interactions with XML-based fault and surface

observation data.– World should develop Open Source Grid/Web services for Geographical

Information Systems as per openGIS specification

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Building PSE’s with theBuilding PSE’s with theRule of the Millisecond IRule of the Millisecond I

Typical Web Services are used in situations with Typical Web Services are used in situations with interaction delays (network transit) of interaction delays (network transit) of 100’s of 100’s of millisecondsmilliseconds

But basic But basic message-based interactionmessage-based interaction architecture only architecture only incurs incurs fraction of a millisecond delayfraction of a millisecond delay

Thus use Web Services to build ALL PSE componentsThus use Web Services to build ALL PSE components• Use messages Use messages and and NOT method/subroutine call or RPCNOT method/subroutine call or RPC

Interaction

Nugget1 Nugget2

Nugget3 Nugget4Data

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Building PSE’s with theBuilding PSE’s with theRule of the Millisecond IIRule of the Millisecond II

Messaging has several advantages over scripting languagesMessaging has several advantages over scripting languages• Collaboration trivial by sharing messagesCollaboration trivial by sharing messages• Software Engineering due to greater modularitySoftware Engineering due to greater modularity• Web Services do/will have wonderful supportWeb Services do/will have wonderful support

““Loose” Application couplingLoose” Application coupling uses workflow technologies uses workflow technologies Find characteristic interaction timeFind characteristic interaction time (millisecond programs; (millisecond programs;

microseconds MPI and particle) and use microseconds MPI and particle) and use best supported best supported architecture at this levelarchitecture at this level• Two levels: Web Service (Grid) Two levels: Web Service (Grid) and and

C/C++/C#/Fortran/Java/PythonC/C++/C#/Fortran/Java/Python Major difficultyMajor difficulty in frameworks is NOT building them but rather in in frameworks is NOT building them but rather in

supporting themsupporting them• IMHO only hope is to always IMHO only hope is to always minimize life-cycle support risksminimize life-cycle support risks• Simulation/science is too small a field to support much!Simulation/science is too small a field to support much!

Expect to use DIFFERENT technologies at each level Expect to use DIFFERENT technologies at each level even though even though possible to do everything with one technologypossible to do everything with one technology• Trade off support versus performance/customizationTrade off support versus performance/customization

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Why we can dream of using HTTP and that slow stuff

We have at least three tiers in computing environment Client (user portal) “Middle Tier” (Web Servers/brokers) Back end (databases, files, computers etc.) In Grid programming, we use HTTP (and used to use

CORBA and Java RMI) in middle tier ONLY to manipulate a proxy for real job• Proxy holds metadata • Control communication in middle tier only uses metadata• “Real” (data transfer) high performance communication in

back end

3030

Integration of Data and Filters One has the OGSA-DAI Data repository interface combined

with WSDL of the (Perl, Fortran, Python …) filter User only sees WSDL not data syntax Some non-trivial issues as to where the filtering compute

power is• Microsoft says filter next to data

DBFilter

WSDL

Of Filter

OGSA-DAI

Interface

3131

HPCSimulation

DataFilter

Data FilterD

ata

Filt

er

Data

Filter

Data

Filter

Distributed Filters massage dataFor simulation

Other

Grid

and W

eb

Servi

ces

AnalysisControl

Visualize

SERVOGrid (Complexity) Computing Model

Grid

OGSA-DAIGrid Services

This Type of Gridintegrates with

Parallel computingMultiple HPC

facilities but only use one at a time

Many simultaneous data sources and

sinks

Grid Data Assimilation

3232

Data Assimilation Data assimilation implies one is solving some optimization

problem which might have Kalman Filter like structure

Due to data deluge, one will become more and more dominated by the data (Nobs much larger than number of simulation points).

Natural approach is to form for each local (position, time) patch the “important” data combinations so that optimization doesn’t waste time on large error or insensitive data.

Data reduction done in natural distributed fashion NOT on HPC machine as distributed computing most cost effective if calculations essentially independent • Filter functions must be transmitted from HPC machine

2 2

1

min ( , ) _obsN

i iTheoretical Unknownsi

Data position time Simulated Value Error

3333

Distributed Filtering

HPC Machine

Distributed Machine

Data FilterNobslocal patch 1

Nfilteredlocal patch 1

Data FilterNobslocal patch 2

Nfilteredlocal patch 2

GeographicallyDistributedSensor patches

Nobslocal patch >> Nfiltered

local patch ≈ Number_of_Unknownslocal patch

Send needed FilterReceive filtered data

In simplest approach, filtered data gotten by linear transformations on original data based on Singular Value Decomposition of Least squares matrix

Factorize Matrixto product oflocal patches

3434

Two-level Programming I The paradigm implicitly assumes a two-level

Programming Model We make a Service (same as a “distributed object” or

“computer program” running on a remote computer) using conventional technologies• C++ Java or Fortran Monte Carlo module• Data streaming from a sensor or Satellite• Specialized (JDBC) database access

Such services accept and produce data from users files and databases

The Grid is built by coordinating such services assuming we have solved problem of programming the service

Service Data

3535

Two-level Programming II The Grid is discussing the composition of distributed

services with the runtime interfaces to Grid as opposed to UNIX pipes/data streams

Familiar from use of UNIX Shell, PERL or Python scripts to produce real applications from core programs

Such interpretative environments are the single processor analog of Grid Programming

Some projects like GrADS from Rice University are looking at integration between service and composition levels but dominant effort looks at each level separately

Service1 Service2

Service3 Service4

3636

Conclusions Grids are inevitable and pervasive Simulations should build on commodity technology Can expect Web Services and Grids to merge with a common

set of general principles but different implementations with different scaling and functionality trade-offs

We will be flooded with data, information and purported knowledge

Re-examine where to use data and where to use simulation• Double the size of your supercomputer versus integrating sensors with

it! Should be re-examining software architectures – use explicit

messaging where-ever possible PSE’s, HLA, Command and Control, GIS, Collaboration,

data federation all are impacted by service based architectures

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Grid Computing: Making The Global Infrastructure a Reality

Based on work done in preparing book edited withFran Berman andAnthony J.G. Hey,

ISBN: 0-470-85319-0 Hardcover 1080 Pages Published March 2003 http://www.grid2002.org

3838

Other References See the webcast in an Oracle technology series

http://webevents.broadcast.com/techtarget/Oracle/100303/index.asp?loc=10 See also the “Gap Analysis”

http://grids.ucs.indiana.edu/ptliupages/publications/GapAnalysis30June03v2.pdf

• I can send you nicely printed versions of this• End of this is a good collection of references and it gives both

a general survey of current Grids and specific examples from UK

Appendix with more details is:http://grids.ucs.indiana.edu/ptliupages/publications/Appendix30June03.pdf

White Paper on Grids in DoD http://grids.ucs.indiana.edu/ptliupages/publications/DODe-ScienceGrids.pdf

See also GlobusWorld http://www.globusworld.org/ and the Grid Forum http://www.gridforum.org