Web and Grid Services from Web and Grid Services from Pitt/CMUPitt/CMU

Andrew ConnollyAndrew Connolly

Department of Physics and Department of Physics and AstronomyAstronomy

University of PittsburghUniversity of PittsburghJeff Gardner, Alex Gray, Simon Jeff Gardner, Alex Gray, Simon Krughoff, Andrew Moore, Bob Nichol, Krughoff, Andrew Moore, Bob Nichol, Jeff SchneiderJeff Schneider

Serial and Parallel Serial and Parallel ApplicationsApplications

ITR collaboration: University of Pittsburgh and Carnegie Mellon ITR collaboration: University of Pittsburgh and Carnegie Mellon University.University.

Astronomers, Computer Scientists and StatisticiansAstronomers, Computer Scientists and Statisticians Developing fast, usually tree-based, algorithms to reduce large Developing fast, usually tree-based, algorithms to reduce large

volumes of data.volumes of data. Sample projects:Sample projects:

Source detection and cross matchSource detection and cross match Parallel Correlation FunctionsParallel Correlation Functions Parallelized serial applications (through GridShell on the Parallelized serial applications (through GridShell on the

Teragrid)Teragrid) Object classification (Morphologies and general Object classification (Morphologies and general

classification)classification) Anomaly findingAnomaly finding Density estimationDensity estimation Intersections in parameter spaceIntersections in parameter space

WSExtractor: Source WSExtractor: Source DetectionDetection

Wrapping existing services as webservicesWrapping existing services as webservices Accessible through client and webservicesAccessible through client and webservices

Prototype Sextractor (and interface to other services)Prototype Sextractor (and interface to other services) Communication through SOAP messages and Communication through SOAP messages and

attachmentsattachments Full configurable through webservice interfaceFull configurable through webservice interface Outputs VOTablesOutputs VOTables Dynamically cross matches with openskyquery.netDynamically cross matches with openskyquery.net Returns cross matched VOTablesReturns cross matched VOTables Plots through Aladin and VOPlotPlots through Aladin and VOPlot

WSext

IssuesIssues

VOTable and JavaVOTable and Java DataHandler typeDataHandler type

Working with C and .NETWorking with C and .NET Aladin as applet on serverAladin as applet on server

Eliminates multiple transfers of imagesEliminates multiple transfers of images Concurrent Access – sessions with WSConcurrent Access – sessions with WS Value validationValue validation

Harnessing Harnessing parallel grid resources in the NVO parallel grid resources in the NVO data mining frameworkdata mining framework

N-pt correlation functions for SDSS data:N-pt correlation functions for SDSS data: 2-pt: hours2-pt: hours 3-pt: weeks3-pt: weeks 4-pt: 100 years!4-pt: 100 years!

● With the NVO, computational requirements will be much With the NVO, computational requirements will be much more extreme.more extreme.

● There will be many more problems for which throughput There will be many more problems for which throughput can be substantially enhanced by can be substantially enhanced by parallel computersparallel computers..

Grid ServicesGrid Services

Parallel programs are hard to write!Parallel programs are hard to write! Parallel codes (especially massively parallel ones) are Parallel codes (especially massively parallel ones) are

used by a limited number of "boutique enterprises" used by a limited number of "boutique enterprises" with the resources to develop them.with the resources to develop them.

Scientific programming is a battle between run Scientific programming is a battle between run time and development time: time and development time: Development time must be less than run time!Development time must be less than run time!

Large development time means they must be reused Large development time means they must be reused again and again and again to make the development again and again and again to make the development worth it (e.g. N-body hydrodynamic code).worth it (e.g. N-body hydrodynamic code).

Even the "boutiques" find it extraodinarily difficult Even the "boutiques" find it extraodinarily difficult to conduct new queries in parallel. to conduct new queries in parallel.

For example, all of the U.Washington "N-Body Shop's" For example, all of the U.Washington "N-Body Shop's" data analysis code is data analysis code is stillstill serial. serial.

The challenge of ParallelismThe challenge of Parallelism

Canned routines (e.g. "density calculator", "cluster finder", "2-pt Canned routines (e.g. "density calculator", "cluster finder", "2-pt correlation function") restrict inquery space.correlation function") restrict inquery space.

Bring the power of a distributed TeraScale computing grid to Bring the power of a distributed TeraScale computing grid to NVO users. Provide NVO users. Provide seamless scalabilityseamless scalability from single processor from single processor machines to TeraFlop platforms machines to TeraFlop platforms while not restricting inquery while not restricting inquery spacespace..

Toolkit:Toolkit: Highly generalHighly general Highly flexibleHighly flexible Minimizes development timeMinimizes development time Efficiently and scalably parallelEfficiently and scalably parallel Optimized for common architectures (MPI, SHMEM, POSIX, etc)Optimized for common architectures (MPI, SHMEM, POSIX, etc)

A Scalable Parallel Analysis A Scalable Parallel Analysis FrameworkFramework

Identify Critical Abstraction Sets and Critical Identify Critical Abstraction Sets and Critical Methods Sets from work done on serial Methods Sets from work done on serial algorithm research by existing ITR.algorithm research by existing ITR. Efficiently parallelize these abstraction and Efficiently parallelize these abstraction and

methods setsmethods sets Distribute in the form of a parallel toolkit.Distribute in the form of a parallel toolkit.

Developing a fast serial algorithm is completely Developing a fast serial algorithm is completely different than implementing that algorithm in different than implementing that algorithm in parallel.parallel.

MethodologyMethodology

Developing an efficient & scalable parallel 2-, 3-, Developing an efficient & scalable parallel 2-, 3-, 4-point correlation function code.4-point correlation function code.

Development on Development on Terascale Computing SystemTerascale Computing System Based on the parallel gravity code PKDGRAV:Based on the parallel gravity code PKDGRAV:

Highly portable (MPI, POSIX Threads, SHMEM, & Highly portable (MPI, POSIX Threads, SHMEM, & others)others)

Highly scalableHighly scalable

Parallel NptParallel Npt

92% linear speedupon 512 PEs!

By Amdahl's Law,this means 0.017%of the code isactually serial.

So far, only74% speedupon 128 PEs

2-pt Correlation Function (2o)Parallel Npt performanceParallel Npt performance

2-pt Correlation Function (2o)Parallel Npt PerformanceParallel Npt Performance

Issues and Future directionsIssues and Future directions

Problem is communication latenciesProblem is communication latencies N-body inter-node communication small (npt large)N-body inter-node communication small (npt large) Minimize off processor communicationMinimize off processor communication Extend to npt (in progress)Extend to npt (in progress) Adaptive learning for allocating resources and Adaptive learning for allocating resources and

work loadwork load Interface with webservicesInterface with webservices

Not another IRAFNot another IRAF