Providing HPC Resources forProviding HPC Resources forPhilips Research and PartnersPhilips Research and Partners

Ronald van DrielRonald van Driel

Senior TechnologistSenior Technologist

Research ICTResearch ICT

April 23April 23rdrd, 2007, 2007

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Agenda

• Introduction Philips and Research

• Research Interest in HPC

• HPC and Grid Related Activities

• Vision on Future Directions

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Royal Philips Electronics

• One of the largest global electronics company with sales in– 2006 of EUR 26,976 million– Q4 2006 of EUR 8,128 million

• Founded in 1891

• Multinational workforce of 121,700 employees (January 2007)

• Active in the areas of Healthcare, Lifestyle and Technology

• Manufacturing sites in 28 countries, sales outlets in 150 countries

• R&D expenditures EUR 1,619 mln (2006)

Headquarters: Amsterdam, The Netherlands

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Significant new product introductions in 2006/2007

Medical Systems Lighting

Consumer Electronics

DAP

EP Navigator

Gemini Time-of-Flight

PET/CT

SureSigns VM

BrightView SPECT

Aluminum Juicer

Wardrobe care

Williams F1 shaver

Wake up lightVOIP

phone

Portable Media Devices

Ambient Experience

HD Category

ActiLume

Edore

Living Colours

Luxeon ® K2LED

Cosmopolis

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Meet Philips Research

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Regional Representation

Briarcliff

Redhill

Aachen

Shanghai

Eindhoven

Bangalore

Hamburg

Staffed by around 1,800 people

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1891 19271930’s

1960’s 1981

1990’s 2000’s 2004

2006

Track Record in Innovation

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From Closed to Open Innovation

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World-class technology centre of high tech companies working together in the development of new technologies

910,000 sq.m Over 50 nationalities 7,000-8,000 people by 2008 more than EUR 500 million

investment by Philips Comprises a broad set of world-

class facilities and expertise, that are shared with partners:• Micro-and Nanotechnology

infrastructure and services (MiPlaza), HD Studio, EMC3, LSF, etc.

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High Tech Campus EindhovenStrength through diversity & networks

Corporate innovators

Consultancy & services

Research institutes

Economic development companies

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Global Research ICT Organization

CIOPatrick Stemkens

Adaptive & DedicatedICT

(ADICT)

GlobalInfrastructures

BusinessInformation

Systems

InfrastructureDevelopment

InfrastructureManagement

InformationServices

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Agenda

• Introduction Philips and Research

• Research Interest in HPC

• HPC and Grid Related Activities

• Vision on Future Directions

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History of Central Compute Facilities at Philips Research Eindhoven

1980 1985 1990 1995 2000 2005

IBM Mainframe – VM/CMS & MVS

VAX/VMS

Apollo Workstations

GRID

Central Compute Cluster (NXA)HP-UX Linux

Silicon Graphics – Video/Audio/Graphics

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Observations I

• Developments in application fieldse.g. Medical Imaging and Bioinformatics

– Many sensors and high-resolution sensors: data explosion– Correlation of data from different sources (e.g. SPECT/CT)

• Open Innovation– Need to cooperate to get resources and/or knowledge in the

right place at the right time: “Support the technology chain”

• Infrastructure– Cope with the “data explosion” and “large scale data analysis”– Large computations, but much can be parallelized or distributed– Must facilitate secure collaboration with partners

From model driven to data driven experiments

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Observations II

Cannot be done by a single entityMust share and cooperate

Big growth in application complexity. Resource needs of some application fields may grow orders of magnitudeWill lead to huge growth in data storage and compute capacity needs.

And today's business innovation is done in an Open Innovation setting: Collaboration in virtual teams is the way of working.(Industry, Universities & Institutes) Needs to be supported by applications and IT infrastructures.

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IT Resources Importance I

Storage:The ability to cope with very large and often distributed datasets.

Visualization: "Virtual reality" techniques are becoming an increasingly important part of applications or workflows.

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IT Resources Importance II

Compute: A substantial increase in demand for computer power and the ability to run data intensive tasks.

Networks:Enabling technology for providing fast access to distributed resources.

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Research Interest in HPC?

• A number of program, project and application fields at Philips Research will require (or may benefit from) high performance computing:– Medical imaging to improve image quality and accuracy.– Molecular Medicine and Bioinformatics will be faced with an

explosive growth of data.– Large simulations and inverse problems

• Lighting armature optics for solid state lighting

– System in Package (SIP) and Solid State Lighting may need to introduce 3D models and simulations.

• HPC capabilities might yield attractive added value for potential partners of Philips Research.– Important to be part of the ECO system

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Agenda

• Introduction Philips and Research

• Research Interest in HPC

• HPC and Grid Related Activities

• Vision on Future Directions

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Research ICT HPC and Grid Activities

• Philips Research ICT is trying to close the gap between “demanding” jobs that researchers have and the supporting IT infrastructures.

• Translate upcoming IT technology in new strategic options• Developed various HPC/grid demonstrator aimed at

helping researchers to do their calculations faster…• Installed a cluster that is connect to DutchGrid to

understand and investigate the technology– Access via “gLite” or “UNICORE” grid middleware software– Allows for more demanding, jobs to be sent to other (possibly

external) “clusters”

• Participate in “Virtual Laboratory for e-Science” (VL-e) and “BIG-GRID” projects

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Example: Researchers Workflow

Short cycles require maximum flexibility: PC/WS or local cluster

Secure access to large data and compute resources: shared on a site. Debug possibilitiesUsually executed on a “Site Cluster”

Full simulations using specialized data, compute and visualization infrastructures.

• E.g. medical imaging, bioinformatics simulation and search, 3D multi-physics modeling, system in package simulation, etc.

• Many projects do not follow the complete flow, but stay at one or more stages.

Application dependent solutions e.g. dedicated computers or a bunch of chips

Workflow View

Experimenting with algorithmor numerical set-up

Validation and regression tests

Pre-production tests withfull implementation

Production implementation

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Example: Researchers Workflow

Short cycles require maximum flexibility: PC/WS or local cluster

Secure access to large data and compute resources: shared on a site. Debug possibilitiesThis “Site Cluster” is part of “DutchGrid”!

Full simulations using specialized data, compute and visualization infrastructures.

• E.g. medical imaging, bioinformatics simulation and search, 3D multi-physics modeling, system in package simulation, etc.

• Many projects do not follow the complete flow, but stay at one or more stages.

Application dependent solutions e.g. dedicated computers or a bunch of chips

Workflow View

Experimenting with algorithmor numerical set-up

Validation and regression tests

Pre-production tests withfull implementation

Production implementation

Site (small) cluster at HTC Eindhoven Part of VL-e infrastructure Infrastructure provider for VL-e Maximum control and fast feedback Priority setting and security can be guaranteed

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Grid Demonstrator – Aachen / Eindhoven

• PET System Simulation• Monte Carlo simulation of the

scanner up to the detection of scintillation light– ~32 CPU days on 2.8 GHz Xeon– 100 x 1 GB output

• System-level simulation of the detector electronics (Mona/Lisa)– 2 phase post processing

• Simulation can be highly parallelized

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PET Simulation Workflow Schematic

motronI2 O2

motronI.. O..

motronI1 O1

I

MotronS

IN S

Mona

MCA C2

MCA C..

MCA C1

1 GB

80 MB

100 MB

< 1 KB

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Cluster/Server/WorkStationCluster/Server/WorkStation

Meta-Scheduler / Scheduler

OpenPBS, SGE, LSF, Condor,Others (SDK)

GridAgent

GridXpert Synergy : Architecture

GridManager(J2EE) PKI RDBMS

GridXpert Synergy:

• GridManager– Full J2EE standards– Embedded Certificate

Authority / PKI for all resources and users

– API available• Enterprise portal (EIP)• Import/export XML

modelization

• GridAgent– Job Management– 3rd party data transfer

between GridAgent (GridFTP)

– Based on Globus development

– Integration with lead schedulers

Web Browser

Jdbc

HTTPS / RSL Globus

HTTPS / HTML Soap

ApplicationXML

CLI

Meta-Scheduler / Scheduler

OpenPBS, SGE, LSF, Condor,Others (SDK)

GridAgent

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Philips jobs at NIKHEF

SPECT Simulation

• 20,000 jobs of ~3.2 CPU hours each• Executed at NIKHEF and SARA clusters• Access via LCG-2 Grid middleware software• Single Photon Emission Computed Tomography

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Grid Portal for PET System SimulationPositron Emission Tomography• Application developed by

Researcher in Aachen.• Design (graphical/web)

portals– Core services too complex

to present to scientists

• Based on GridSphere web portal technology (JSR-168)

• Initial version developed with assistance from GridwiseTech

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Simulate Full HDTV Data Processing Chain

• Picture quality enhancement• Picture rate-up conversion• Pixel plus …

Input OutputAlgorithm 1 Algorithm n

~0.5 TB ~1500 CPU hours ~2.2 TB

Original (Frame repetition)

Measured > 50 MB/s data transfer rates between Amsterdam and Eindhoven over GigaPort connection

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GAMA ResearchHealthcare Systems Architecture research group

WindowsIDL, Pride

LinuxGlobus

GatewayClient

LAN LANDutchGridResources

The GAMA architecture: computational Grids• Architecture for solving compute-intensive medical applications • Minimally invasive: Running on Grid as alternative, easy fall back to local versions• Simultaneously provides different sets of services to multiple users and applications• Adaptive to various healthcare applications• Example: Brain imaging, the High Angular Fiber tracking (HAFT) application

Clinical trials of HAFT software at AMC are being executed on VL-e cluster at HTC in Eindhoven.

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VL-e Cluster HTC EindhovenSuper

ComputeCenters

BIG GRID,Sara,

Jülich, etceteras

Fas

t In

tern

et

Bioinformaticsinfrastructure

Internet

DutchGrid

Secure Environment extern Philips

Compute Cluster

Cluster Load Balancing

Grid Access LayerExternal

Oracle

Database

Computeservers

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UNICOREUniform Interface to Computing Resources• Speedup Monte Carlo based

simulation package developed on Microsoft Windows platform

• Ported application to Linux• Allows for easy parallelization

using loosely coupled jobs• Deploy UNICORN client on

Microsoft Windows platform• Python script to create XML file

that is loaded in OpenMolGRID• Project defined to developed

application specific UNICORE plug-in

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Agenda

• Introduction Philips and Research

• Research Interest in HPC

• HPC and Grid Related Activities

• Vision on Future Directions

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Vision: Shared Resources and InfrastructureEnd-user view

• Applications “flow through infrastructure” and can be run and used anywhere:

Boosts sharing and collaboration

• Users decide whether to keep data in internal secure environment or to put in shared space

• Applications might be split in secure and non-secure parts

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Vision: Shared Resources and Infrastructure Infrastructure view

• Grid is next generation shared infrastructure comprising distributed data storage capacity, (visualization) equipment and compute clusters:– Capitalize on our extensive experiences with

centralized compute and data clusters– Other equipment will shift to the shared infrastructure

too (e.g. VL-e)

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Vision: Shared Resources and Infrastructure Success factors

• End-user guidance and support is essential!!• Deploy workbenches for application fields• Continuous development, be a leader• Standardisation of middleware layer

– e.g. gLite, UNICORE and Globus

• QoS and AAA are key in e-Science production infrastructures

• Basic building blocks are data storage and (low latency) compute clusters

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Questions?

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