Grid Computing: like herding cats?

1. Grid Computing: like herding cats? • Stephen Jarvis • High Performance Systems Group • University of Warwick, UK

2. Sessions on Grid • What are we going to cover today? • A brief history • Why we are doing it • Applications • Users • Challenges • Middleware • What are you going to cover next week? • technical talk on the specifics of our work • Including application to e-Business and e-Science

3. An Overused Analogy • Electrical Power Grid • Computing power might somehow be like electrical power • plug in • switch on • have access to unlimited power • We don’t know who supplies the power, or where it comes from • just pick up the bill at the end of the month • Is this the future of computing?

4. Sounds great - but how long? • Is the computing infrastructure available? • Computing power • 1986: Cray X-MP ($8M) • 2000: Nintendo-64 ($149) • 2003: Earth Simulator (NEC), ASCI Q (LANL) • 2005: Blue Gene/L (IBM), 360 Teraflops • Look at www.top500.org for current supercomputers!

5. Storage & Network • Storage capabilities • 1986: Local data stores (MB) • 2002: Goddard Earth Observation System – 29TB • Network capabilities • 1986 : NFSNET 56Kb/s backbone • 1990s: Upgraded to 45Mb/s (gave us the Internet) • 2000s: 40 Gb/s

6. Space telescopes Terra-byte databases Millions of PCs 30% Utilisation 50M Mobile Phones Supercomputing Centres 10k PS/2 per week Many Potential Resources GRID

7. Some History: NASAs Information Power Grid • The vision … mid ’90s • to promote a revolution in how NASA addresses large-scale science and engineering • by providing a persistent HPC infrastructure • Computing and data management services • on-demand • locate and co-schedule multi-Center resources • address large-scale and/or widely distributed problems • Ancillary services • workflow management and coordination • security, charging …

8. Human Models Whole system simulations are produced by couplingall of the sub-system simulations • Lift Capabilities • Drag Capabilities • Responsiveness Stabilizer Models Airframe Models Crew Capabilities - accuracy - perception - stamina - re-action times - SOP’s Engine Models • Braking performance • Steering capabilities • Traction • Dampening capabilities • Thrust performance • Reverse Thrust performance • Responsiveness • Fuel Consumption Landing Gear Models

9. 300 node Condor pool MCAT/SRB MDS CA DMF Boeing O2000 cluster MDS EDC GRC O2000 NGIX CMU NREN NCSA GSFC LaRC JPL O2000 cluster SDSC NTON-II/SuperNet MSFC MDS O2000 JSC KSC

10. Engine Models Wing Models Human Models National Air Space Simulation Environment Stabilizer Models GRC 44,000 Wing Runs 50,000 Engine Runs Airframe Models 66,000 Stabilizer Runs ARC LaRC Virtual National Air Space VNAS 22,000 Commercial US Flights a day 22,000 Airframe Impact Runs • FAA Ops Data • Weather Data • Airline Schedule Data • Digital Flight Data • Radar Tracks • Terrain Data • Surface Data Simulation Drivers 48,000 Human Crew Runs 132,000 Landing/ Take-off Gear Runs (Being pulled together under the NASA AvSP Aviation ExtraNet (AEN) Landing Gear Models

11. What is a Computational Grid? • A computational grid is a hardware and software infrastructure that provides dependable, consistent, pervasive and inexpensive access to high-end computational capabilities. • The capabilities need not be high end. • The infrastructure needs to be relatively transparent.

12. Selected Grid Projects • US Based • NASA Information Power Grid • DARPA CoABS Grid • DOE Science Grid • NSF National Virtual Observatory • NSF GriPhyN • DOE Particle Physics Data Grid • NSF DTF TeraGrid • DOE ASCI DISCOM Grid • DOE Earth Systems Grid etc… • EU Based • DataGrid (CERN, ..) • EuroGrid (Unicore) • Damien (Metacomputing) • DataTag (TransAtlanticTestbed, …) • Astrophysical Virtual Observatory • GRIP (Globus/Unicore) • GRIA (Industrial applications) • GridLab (Cactus Toolkit, ..) • CrossGrid (Infrastructure Components) • EGSO (Solar Physics) • Other National Projects • UK - e-Science Grid • Netherlands – VLAM-G, DutchGrid • Germany – UNICORE Grid, D-Grid • France – Etoile Grid • Italy – INFN Grid • Eire – Grid-Ireland • Scandinavia - NorduGrid • Poland – PIONIER Grid • Hungary – DemoGrid • Japan – JpGrid, ITBL • South Korea – N*Grid • Australia – Nimrod-G, …. • Thailand • Singapore • AsiaPacific Grid

13. The Big Spend: two examples • US Tera Grid • $100 Million US Dollars (so far…) • 5 supercomputer centres • New ultra-fast optical network ≤ 40Gb/s • Grid software and parallel middleware • Coordinated virtual organisations • Scientific applications and users • UK e-Science Grid • £250 Million (so far…) • Regional e-Science centres • New infrastructure • Middleware development • Big science projects SuperJANET4

14. e-Science Grid Edinburgh Glasgow DL Newcastle Lancaster White Rose Belfast Manchester Birmingham/Warwick Cambridge Oxford UCL Bristol RL Hinxton Cardiff London Soton

15. Who wants Grids and why? • NASA • Aerospace simulations, Air traffic control • NWS, In-aircraft computing • Virtual Airspace • Free fly, Accident prevention • IBM • On-demand computing infrastructure • Protect software • Support business computing • Governments • Simulation experiments • Biodiversity, genomics, military, space science…

16. Classes of Grid applications

17. Classes of Grid

18. In Real Time Scientific Information Scientific Discovery Real Time Integration Workflow Construction Operational Data Literature Instrument Data Databases Interactive Visual Analysis Dynamic Application Integration Using Distributed Resources Images Discovery Net Project

19. KEGG Inter Pro SMART Execute distributed annotation workflow SWISS PROT EMBL NCBI TIGR SNP GO Nucleotide Annotation Workflows Download sequence from Reference Server Save to Distributed Annotation Server • 1800 clicks • 500 Web access • 200 copy/paste • 3 weeks work in 1 workflow and few second execution

20. Grand Challenge: Integrating Different Levels of Simulation molecular cellular organism Sansom et al. (2000) Trends Biochem. Sci. 25:368 • An e-science challenge – non-trivial • NASA IPG as a possible paradigm • Need to integrate rigorously if to deliver accurate & hence biomedically useful results Noble (2002) Nature Rev. Mol. Cell.Biol. 3:460

21. Classes of Grid users

22. Grid architecture • Composed of hierarchy of sub-systems • Scalability is vital • Key elements: • End systems • Single compute nodes, storage systems, IO devices etc. • Clusters • Homogeneous networks of workstations; parallel & distributed management • Intranet • Heterogeneous collections of clusters; geographically distributed • Internet • Interconnected intranets; no centralised control

23. End Systems • State of the art • Privileged OS; complete control of resources and services • Integrated nature allows high performance • Plenty of high level languages and tool • Future directions • Lack features for integration into larger systems • OS support for distributed computation • Mobile code (sandboxing) • Reduction in network overheads

24. Clusters • State of the art • High-speed LAN, 100s or 1000s of nodes • Single administrative domain • Programming libraries like MPI • Inter-process communication, co-scheduling • Future directions • Performance improvements • OS support

25. Intranets • State of the art • Grids of many resources, but one admin. domain • Management of heterogeneous resources • Data sharing (e.g. databases, web services) • Supporting software environments inc. CORBA • Load sharing systems such as LSF and Condor • Resource discovery • Future directions • Increasing complexity (physical scale etc) • Performance • Lack of global knowledge

26. Internets • State of the art • Geographical distribution, no central control • Data sharing is very successful • Management is difficult • Future directions • Sharing other computing services (e.g. computation) • Identification of resources • Transparency • Internet services

27. Basic Grid services • Authentication • Can the users use the system; what jobs can they run? • Acquiring resources • What resources are available? • Resource allocation policy; scheduling • Security • Is the data safe? Is the user process safe? • Accounting • Is the service free, or should the user pay?

28. Research Challenges (#1) • Grids computing is a relatively new area • There are many challenges • Nature of Applications • New methods of scientific and business computing • Programming models and tools • Rethinking programming, algorithms, abstraction etc. • Use of software components/services • System Architecture • Minimal demands should be placed on contributing sites • Scalability • Evolution of future systems and services

29. Research Challenges (#2) • Problem solving methods • Latency- and fault-tolerant strategies • Highly concurrent and speculative execution • Resource management • How are the resources shared? • How do we achieve end-to-end performance? • Need to specify QoS requirements • Then need to translate this to resource level • Contention?

30. Research Challenges (#3) • Security • How do we safely share data, resources, tasks? • How is code transferred? • How does licensing work? • Instrumentation and performance • How do we maintain good performance? • How can load-balancing be controlled? • How do we measure grid performance? • Networking and infrastructure • Significant impact on networking • Need to combine high and low bandwidth

31. Development of middleware • Many people see middleware as the vital ingredient • Globus toolkit • Component services for security, resource location, resource management, information services • OGSA • Open Grid Services Architecture • Drawing on web services technology • GGF • International organisation driving Grid development • Contains partners such as Microsoft, IBM, NASA etc.

32. Middleware Conceptual Layers Workload Generation, Visualization… Discovery, Mapping, Scheduling, Security, Accounting… Computing, Storage, Instrumentation…

33. Requirements include: • Offers up useful resources • Accessible and useable resources • Stable and adequately supported • Single user ‘Laptop feel’ Middleware has much of this responsibility

34. Demanding management issues • Users are (currently) likely to be sophisticated • but probably not computer ‘techies’ • Need to hide detail & ‘obscene’ complexity • Provide the vision of access of full resources • Provide contract for level(s) of support (SLAs)

35. Gate Keeper / Manager • Acts as resource manager. • Responsible for mapping applications to resources. • Scheduling tasks. • Ensuring service level agreements (SLAs) • Distributed / Dynamic. Key Interface between Applications & Machines

36. Middleware Projects • Globus, Argonne National Labs, USA • AppLeS, UC San Diego, USA • Open Grid Services Architecture (OGSA) • ICENI, Imperial, UK • Nimrod, Melbourne, Australia • Many others... including us!!

37. HPSG’s approach: • Determine what resources are required • (advertise) • Determine what resources are available • (discovery) • Map requirements to available resources • (scheduling) • Maintain contract of performance • (service level of agreement) • Performance drives the middleware decisions • PACE

38. ‘[The Grid] intends to make access to computing power, scientific data repositories and experimental facilities as easy as the Web makes access to information.’ • High Performance Systems Group, Warwick • www.dcs.warwick.ac.uk/research/hpsg Tony Blair, 2002

39. And herding cats … • 100,000s computers • Sat. links, miles of networking • Space telescopes, atomic colliders, medical scanners • Tera-bytes of data • Software stack a mile high…