Grid Tutorial
Norbert Podhorszki
Part I. What are Grids and e-Science?
EGEE is funded by the European Union under contract IST-2003-508833 EGEE Tutorial at University of Szeged? – Dec 7th, 2004 - 1
Acknowledgements
• This talk is based on a module of the tutorials delivered by the EGEE training team and slides from
• • • • • • • • • •
Andrew Grimshaw, University of Virginia Bob Jones, EGEE Technical Director Mark Parsons, EPCC the EDG training team Roberto Barbera, INFN Ian Foster, Argonne National Laboratories Jeffrey Grethe, SDSC The National e-Science Centre David Fergusson, ??? Peter Kacsuk, MTA SZTAKI
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Goals of Part I
• Introduce grid concepts and definitions
• Why Grids?
• A brief outline of history leading to EGEE
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Overview
• What is different about grids?
• Characteristics of a grid
• eScience
• Applications (what’s in it for the working scientist)
• European grids, and the world
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What is different about grids?
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What is Grid Computing?
• A Virtual Organisation is:
•
People from different institutions working to solve a common goal • Sharing distributed processing and data resources
• Grid infrastructure enables virtual organisations
“Grid computing is coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations” (I.Foster)
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Grids vs. Distributed Computing?
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A Real World Distributed Application
• SETI@home
•
• •
3.8M users in 226 countries
1200 CPU years/day 38 TF sustained (Japanese Earth Simulator is 40 TF peak)
•
•
1.7 ZETAflop over last 3 years (10^21, beyond peta and exa …)
Highly heterogeneous: >77 different processor types
Credit to Fran Berman
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Grids vs. Distributed Computing
• Distributed applications already exist, but they tend to be specialised systems intended for a single purpose or user group • Grids go further and take into account:
• •
Different kinds of resources
• Not always the same hardware, data and applications
Different kinds of interactions
• User groups or applications want to interact with Grids in different ways
•
Dynamic nature
• Resources and users added/removed/changed frequently
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Grid vs. metacomputing
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Motivations
metacomputing
• Grand challenge problems run weeks and months even on supercomputers and clusters
Grid
+ To form a computational grid similar to the information data access on the web. • Any computers/devices must be connected by wide area networks in order to form a universal source of computing power.
• Various supercomputers/clusters must be connected by wide area networks in order to solve grand challenge problems in reasonable time
• Grid = generalised metacomputing
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Original meaning of metacomputing
Metacomputing
Super = computing
Wide area + network
Original goal of metacomputing:
• Distributed supercomputing to achieve higher performance than individual supercomputers/clusters can provide
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Distributed Supercomputing
NCSA Origin Argonne SP
Caltech Exemplar
• Issues:
• • •
Maui SP
•
• •
Resource discovery, scheduling Configuration Multiple communiation methods Message passing (MPI) Scalability Fault tolerance
SF-Express Distributed Interactive Simulation: Caltech, USC/ISI
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What is a Metacomputer?
• A metacomputer is a collection of
•
•
• • •
computers that are heterogeneous in every aspects geographically distributed connected by a wide-area network form the image of a single computer network based distributed supercomputing
• Metacomputing means:
• •
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What is a Grid?
• A Grid is a collection of
•
•
• • •
computers, storage and other devices that are heterogeneous in every aspects geographically distributed connected by a wide-area network form the image of a single computer network based distributed computing
• Generalised metacomputing means:
• •
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Distributed Supercomputing
NCSA Origin Argonne SP
Caltech Exemplar
• Issues:
Maui SP
• • • •
•
•
Resource discovery, scheduling Configuration Multiple comm methods Message passing (MPI) Scalability Fault tolerance
SF-Express Distributed Interactive Simulation: Caltech, USC/ISI
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High-Throughput Computing
• Schedule many independent tasks
Deadline
•
Cost
Parameter studies • Data analysis
• Issues:
•
Available Machines
Resource discovery • Data Access • Scheduling • Reservation • Security • Accounting • Code management
Nimrod-G: Monash University
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Characteristics of a grid
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What are the characteristics of a Grid system?
Numerous Resources Ownership by Mutually Distrustful Organizations & Individuals Connected by Heterogeneous, Multi-Level Networks
Different Security Requirements & Policies Required
Different Resource Management Policies
Potentially Faulty Resources Resources are Heterogeneous
Geographically Separated
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What are the characteristics of a Grid system?
Numerous Resources Ownership by Mutually Distrustful Organizations & Individuals Connected by Heterogeneous, Multi-Level Networks
Different Security Requirements & Policies Required
Different Resource Management Policies
Potentially Faulty Resources Resources are Heterogeneous
Geographically Separated
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How Different 2004 is from 1994
• Moore’s law everywhere
• •
Instruments, detectors, sensors, scanners, … Organising their effective use is the challenge For an increasing number of communities Gating step is not collection but analysis Moore’s law gives us all supercomputers Organising their effective use is the challenge Global optical networks Bottlenecks: last kilometre & firewalls
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Enormous quantities of data: Petabytes
• •
• Huge quantities of computing: >100 Top/s
• •
• Ultra-high-speed networks: >10 Gb/s
• •
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Exponential Growth
Performance per Dollar Spent
Doubling Time
(months) 9 12 18
Optical Fibre
(bits per second)
Gilder’s Law (32X in 4 yrs) Storage Law (16X in 4yrs)
Data Storage
(bits per sq. inch)
Chip capacity
(# transistors)
Moore’s Law (5X in 4yrs)
3 4 5
0
1
2
Number of Years
Triumph of Light – Scientific American. George Stix, January 2001
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The main drivers behind Grid
• The relentless increase in microprocessor performance
•
you can buy multi-gigaflop systems for less than €800 in Europe the GEANT network links 32 countries at speeds of up to 10Gbps (and beyond) in the UK we have gone from 100Mbps -> 10Gbps academic backbone since 2000 1Gbps is commonly available to the desktop
• The availability of reliable high performance networking
• •
•
• The desire to push the boundaries of scientific discovery by computational analysis and simulation – e-Science
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eScience
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The Emergence of e-Science
• Invention and exploitation of advanced computational methods
•
To generate, curate and analyse research data
• From experiments, observations and simulations • Quality management, preservation and reliable evidence
•
To develop and explore models and simulations
• Computation and data at extreme scales • Trustworthy, economic, timely and relevant results
•
To enable dynamic distributed virtual organisations
• Facilitating collaboration with information and resource sharing • Security, reliability, accountability, manageability and agility
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Why use Grids for Science?
• Scale of the problems
•
Science increasingly done through distributed global collaborations enabled by the internet Very large data collections Terascale computing resources High performance visualisation Connected by high-bandwidth networks
• Grids provide access to:
• • •
•
• e-Science is more than Grid Technology It is what you do with it that counts
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Challenges
• Must share data between thousands of scientists with multiple interests • Must ensure that all data is accessible anywhere, anytime • Must be scalable and remain reliable for more than a decade • Must cope with different access policies • Must ensure data security
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The Grid Vision
Researchers perform their activities regardless geographical location, interact with colleagues, share and access data The Grid: networked data processing centres and ”middleware” software as the “glue” of resources.
Scientific instruments and experiments provide huge amount of data
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The Emergence of Global Knowledge Communities
Slide from Ian Foster’s ssdbm 03 keynote
Applications
(What’s in it for working scientists)
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Grid Applications
• Medical/Healthcare (imaging,
diagnosis and treatment )
• Bioinformatics (study of the human
genome and proteome to understand genetic diseases)
• Nanotechnology (design of new
materials from the molecular scale)
• Engineering (design optimization,
simulation, failure analysis and remote Instrument access and control)
• Natural Resources and the Environment (weather forecasting, earth
observation, modeling and prediction of complex systems)
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CERN: Data intensive science in a large international facility
• The Large Hadron Collider (LHC)
• The most powerful instrument ever built to
investigate elementary particles physics
• Data Challenge:
•
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10 Petabytes/year of data !!! 20 million CDs each year!
Mont Blanc (4810 m)
• Simulation, reconstruction, analysis:
•
LHC data handling requires computing power equivalent to ~100,000 of today's fastest PC processors!
Downtown Geneva
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CrossGrid
• 1. Interactive biomedical simulation and visualization
• 2. Flooding crisis team support • 3. HEP distributed data analysis • 4. Weather forecasting and air pollution modelling
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Connecting People: Access Grid
Remote video
Visualisation
Microphones
Cameras
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European grids And the world
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Major EU GRID projects
European DataGrid (EDG) LHC Computing GRID (LCG) CrossGRID DataTAG GridLab www.edg.org cern.ch/lcg www.crossgrid.org www.datatag.org www.gridlab.org
EUROGRID
European National Projects: • INFNGRID, • UK e-Science Programme, • NorduGrid
www.eurogrid.org
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EU DataGrid at a glance
People 500 registered users 12 Virtual Organisations
Application Testbed ~20 regular sites > 60,000 jobs submitted (since 09/03, release 2.0) Peak >1000 CPUs 6 Mass Storage Systems
21 Certificate Authorities
>600 people trained 456 person-years of effort
170 years funded
Software > 65 use cases 7 major software releases (> 60 in total) > 1,000,000 lines of code Scientific Applications 5 Earth Obs institutes 10 bio-medical apps 6 HEP experiments
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Grid projects
Many Grid development efforts — all over the world
•UK – OGSA-DAI, RealityGrid, GeoDise, •NASA Information Power Grid Comb-e-Chem, DiscoveryNet, DAME, •DOE Science Grid AstroGrid, GridPP, MyGrid, GOLD, eDiamond, Integrative Biology, … •NSF National Virtual Observatory •Netherlands – VLAM, PolderGrid •NSF GriPhyN •Germany – UNICORE, Grid proposal •DOE Particle Physics Data Grid •France – Grid funding approved •NSF TeraGrid •Italy – INFN Grid •DOE ASCI Grid •Eire – Grid proposals •DOE Earth Systems Grid •Switzerland - Network/Grid proposal •DARPA CoABS Grid •DataGrid (CERN, ...) •Hungary – DemoGrid, Grid proposal •NEESGrid •EuroGrid (Unicore) •Norway, Sweden - NorduGrid •DataTag (CERN,…) •DOH BIRN •Astrophysical Virtual Observatory •NSF iVDGL •GRIP (Globus/Unicore) •GRIA (Industrial applications) •GridLab (Cactus Toolkit) •CrossGrid (Infrastructure Components) •EGSO (Solar Physics)
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