1

The Application-Infrastructure Gap

Dynamicand/or

DistributedApplications

A

1

B

1

99

Shared Distributed Infrastructure

2

Bridging the Gap:Grid Technology

Service-oriented applications Wrap applications as

services Compose applications

into workflows Service-oriented

infrastructure Provision physical

resources to support application workloads

ApplnService

ApplnService

Users

Workflows

Composition

Invocation

Provisioning

3

Grid Technology:Service-Oriented Infrastructure

IBM

IBM

Uniform interfaces,security mechanisms,Web service transport,

monitoring

Computers StorageSpecialized resource

UserApplication

UserApplication

UserApplication

IBM

IBM

GRAM GridFTPHost EnvUser Svc

DAIS

Database

ToolTool Reliable

FileTransfer

MyProxy

Host EnvUser Svc

MDS-Index

4

Data Management

SecurityCommonRuntime

Execution Management

Information Services

Web Services

Components

Non-WS

Components

Pre-WSAuthenticationAuthorization

GridFTP

GridResource

Allocation Mgmt(Pre-WS GRAM)

Monitoring& Discovery

System(MDS2)

C CommonLibraries

GT2

WSAuthenticationAuthorization

ReliableFile

Transfer

OGSA-DAI[Tech Preview]

GridResource

Allocation Mgmt(WS GRAM)

Monitoring& Discovery

System(MDS4)

Java WS Core

CommunityAuthorization

ServiceGT3

ReplicaLocationService

XIO

GT3

CredentialManagement

GT4

Python WS Core[contribution]

C WS Core

CommunitySchedulerFramework

[contribution]

DelegationService

GT4

Globus Open Source Grid Software

5

Java Services in Apache AxisPlus GT Libraries and Handlers

YourJavaService

YourPythonService

YourJavaService R

FT

GR

AM

Del

egat

ion

Inde

x

Trig

ger

Arc

hive

r

pyGlobusWS Core

YourC

Service

C WS Core

RLS

Pre

-WS

MD

S

CA

S

Pre

-WS

GR

AM

Sim

pleC

A

MyP

roxy

OG

SA

-DA

I

GT

CP

Grid

FT

P

C Services using GT Libraries and Handlers

SERVER

CLIENT

InteroperableWS-I-compliant

SOAP messaging

YourJavaClient

YourC

Client

YourPythonClient

YourJavaClient

YourC

Client

YourPythonClient

YourJavaClient

YourC

Client

YourPythonClient

YourJavaClient

YourC

Client

YourPythonClient

X.509 credentials =common authentication

Python hosting, GT Libraries

GT4 Components

6

CustomWeb

ServicesWS-Addressing, WSRF,

WS-Notification

CustomWSRF Web

Services

GT4WSRF Web

Services

WSDL, SOAP, WS-Security

User Applications

Reg

istr

yA

dmin

istr

atio

n

GT

4 C

onta

iner

Web Services:Standards, Tools, Interoperability

NEES: Network for Earthquake Engineering Simulation

Links instruments, data, computers, people

8

Scaling:Grid2003Workflows

Genome sequence analysis

Physicsdata

analysis

Sloan digital sky

survey

9

Application Examples

Earth System Grid:O(100TB) online data

STAR: 5 TB transfer(SRM, GridFTP)

NASA/NVO: Mosaicsfrom multiple sources

Fusion Grid:1000s of jobs

10

LIGO Scientific Collaboration

Continuous gravitational waves are expected to be produced by a variety of celestial objects

Only a small fraction of potential sources are known Need to perform blind searches, scanning the regions of

the sky where we have no a priori information of the presence of a source

Wide area, wide frequency searches

Search is performed for potential sources of continuous periodic waves near the Galactic Center and the galactic core

Search for binary inspirals collapsing into black holes. The search is very compute and data intensive

P. Brady, S. Koranda, D. Brown, S. Fairhurst UWMilwaukee, USA, S. Anderson, K. Blackburn, A. Lazzarini, H. Pulapaka, T. Creighton Caltech, USA, G. Gonzalez, Louisiana State University, Many Others involved in the Testbed

11

Montage Montage (NASA and NVO) Deliver science-grade

custom mosaics on demand

Produce mosaics from a wide range of data sources (possibly in different spectra)

User-specified parameters of projection, coordinates, size, rotation and spatial sampling.

B. Berriman, J. Good, A. Laity, Caltech/IPACJ. C. Jacob, D. S. Katz, JPLhttp://montage.ipac. caltech.edu/

Mosaic created by Pegasus based Montage from a run of the M101 galaxy images on the Teragrid.

12

Small Montage Workflow

~1200 nodes

13

Other ApplicationsSouthern California Earthquake Center

• Southern California Earthquake Center (SCEC), in collaboration with the USC Information Sciences Institute, San Diego Supercomputer Center, the Incorporated Research Institutions for Seismology, and the U.S. Geological Survey, is developing the Southern California Earthquake Center Community Modeling Environment (SCEC/CME).

•Create fully three-dimensional (3D) simulations of fault-system dynamics.

•Physics-based simulations can potentially provide enormous practical benefits for assessing and mitigating earthquake risks through Seismic Hazard Analysis (SHA).

•The SCEC/CME system is an integrated geophysical simulation modeling framework that automates the process of selecting, configuring, and executing models of earthquake systems.

Figure 1: Fréchet sensitivity Kernel showing travel path between a Yorba Linda earthquake and the TriNet Station DLA.

Acknowledgments :

Philip Maechling and Vipin Gupta

University Of Southern California

14

Biology Applications

Tomography (NIH-funded project) Derivation of 3D structure from a

series of 2D electron microscopic projection images,

Reconstruction and detailed structural analysis

complex structures like synapses large structures like dendritic

spines. Acquisition and generation of huge

amounts of data Large amount of state-of-the-art

image processing required to segment structures from extraneous background.

Dendrite structure to be rendered byTomography

Work performed by Mei-Hui Su with Mark Ellisman, Steve Peltier, Abel Lin, Thomas Molina (SDSC)

15

BLAST: set of sequence comparison algorithms that are used to

search sequence databases for optimal local alignments to a query

Lead by Veronika Nefedova (ANL) as part of the Paci Data Quest Expedition program

2 major runs were performed using Chimera and Pegasus:

1) 60 genomes (4,000 sequences each), In 24 hours processed Genomes selected

from DOE-sponsored sequencing projects67 CPU-days of processing time

delivered~ 10,000 Grid jobs>200,000 BLAST executions50 GB of data generated

2) 450 genomes processedSpeedup of 5-20 times were achieved

because the compute nodes we used efficiently by keeping the submission of the jobs to the compute cluster constant.

16

Functional MRI Analysis3a.h

align_warp/1

3a.i

3a.s.h

softmean/9

3a.s.i

3a.w

reslice/2

4a.h

align_warp/3

4a.i

4a.s.h 4a.s.i

4a.w

reslice/4

5a.h

align_warp/5

5a.i

5a.s.h 5a.s.i

5a.w

reslice/6

6a.h

align_warp/7

6a.i

6a.s.h 6a.s.i

6a.w

reslice/8

ref.h ref.i

atlas.h atlas.i

slicer/10 slicer/12 slicer/14

atlas_x.jpg

atlas_x.ppm

convert/11

atlas_y.jpg

atlas_y.ppm

convert/13

atlas_z.jpg

atlas_z.ppm

convert/15

GlobalCommunity

18

Domain-independentDomain-dependent

Content

Services

Resources

Experimental apparatus Servers, storage, networks

Metadatacatalog

Dataarchive

Simulationserver

Certificateauthority

Simulationcode

Exptdesign

Telepresencemonitor

SimulationcodeExpt

output

Electronicnotebook

Portalserver

Scaling Up:Service-Oriented Science

19

For More Information

Globus Alliance www.globus.org

Globus Consortium www.globusconsortium.com

Global Grid Forum www.ggf.org

Open Science Grid www.opensciencegrid.org

2nd Editionwww.mkp.com/grid2