CSTB_SuperComputing_Study_Group.ppt

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CSTB_SuperComputing_Study_Group.ppt

  1. 1. Information Centric Super Computing Jim Gray Microsoft Research [email_address] Talk at http://research.microsoft.com/~gray/talks 20 May 2003 Presentation to Committee on the Future of Supercomputing of the National Research Council's Computer Science and Telecommunications Board
  2. 2. Committee Goal <ul><li>… assess the status of supercomputing in the United States , including the characteristics of relevant systems and architecture research in government, industry, and academia and the characteristics of the relevant market. The committee will examine key elements of context--the history of supercomputing, the erosion of research investment, the needs of government agencies for supercomputing capabilities--and assess options for progress . Key historical or causal factors will be identified. The committee will examine the changing nature of problems demanding supercomputing (e.g., weapons design, molecule modeling and simulation, cryptanalysis, bioinformatics, climate modeling) and the implications for systems design. It will seek to understand the role of national security in the supercomputer market and the long-term federal interest in supercomputing. </li></ul>
  3. 3. Summary: It’s the Software… <ul><li>super Computing is Information centric </li></ul><ul><li>Scientific computing is Beowulf computing </li></ul><ul><li>Scientific computing becoming Info-centric. </li></ul><ul><li>Adequate investment in files/OS/networking </li></ul><ul><li>Underinvestment in Scientific Information management and visualization tools. </li></ul><ul><li>Computation Grid moves too much data, DataGrid (or App Grid) is right concept </li></ul>
  4. 4. Thesis <ul><li>Most new information is digital (and old information is being digitized) </li></ul><ul><li>A Computer Science Grand Challenge: </li></ul><ul><ul><li>Capture </li></ul></ul><ul><ul><li>Organize </li></ul></ul><ul><ul><li>Summarize </li></ul></ul><ul><ul><li>Visualize </li></ul></ul><ul><li>This information </li></ul><ul><li>Optimize Human Attention as a resource </li></ul><ul><li>Improve information quality </li></ul>
  5. 5. Information Avalanche <ul><li>The Situation </li></ul><ul><ul><li>We can record everything </li></ul></ul><ul><ul><li>Everything is a LOT! </li></ul></ul><ul><li>The Good news </li></ul><ul><ul><li>Changes science, education, medicine, entertainment,…. </li></ul></ul><ul><ul><li>Shrinks time and space </li></ul></ul><ul><ul><li>Can augment human intelligence </li></ul></ul><ul><li>The Bad News </li></ul><ul><ul><li>The end of privacy </li></ul></ul><ul><ul><li>Cyber Crime / Cyber Terrorism </li></ul></ul><ul><ul><li>Monoculture </li></ul></ul><ul><li>The Technical Challenges </li></ul><ul><ul><li>Amplify human intellect </li></ul></ul><ul><ul><li>Organize, summarize and prioritize information </li></ul></ul><ul><ul><li>Make programming easy </li></ul></ul>
  6. 6. Super Computers <ul><li>You and Others use Every day </li></ul><ul><ul><li>Google, Inktomi,… </li></ul></ul><ul><ul><li>AOL, MSN, Yahoo! </li></ul></ul><ul><ul><li>Hotmail, MSN,… </li></ul></ul><ul><ul><li>eBay, Amazon.com,… </li></ul></ul><ul><li>All are more than 10 Tops </li></ul><ul><li>All more than 1PB </li></ul><ul><li>IntraNets </li></ul><ul><ul><li>Wal-Mart </li></ul></ul><ul><ul><li>Federal Reserve </li></ul></ul><ul><ul><li>Amex </li></ul></ul><ul><ul><li>1 Tflops </li></ul></ul><ul><li>All more than 1PB </li></ul>They are ALL Information Centric
  7. 7. Q: How can I recognize a SuperComputer? A: Costs 10M$ Gordon Bell’s Seven Price Tiers <ul><li>10$: wrist watch computers (sensors) </li></ul><ul><li>100$: pocket/ palm computers (phone/camera) </li></ul><ul><li>1,000$: portable computers (tablet) </li></ul><ul><li>10,000$: personal computers (workstation) </li></ul><ul><li>100,000$: departmental computers (closet) </li></ul><ul><li>1,000,000$: site computers (glass house) </li></ul><ul><li>10,000,000$: regional computers (glass castle SC) </li></ul>Super Computer / “Mainframe” Costs more than 1M$ Must be an array of processors, disks comm ports
  8. 8. Computing is Information Centric that’s why they call it IT <ul><li>Programs capture, organize, abstract, filter, present Information to people. </li></ul><ul><li>Networks carry Information. </li></ul><ul><li>File is wrong abstraction: Information is typed / schematized words, pictures, sounds, arrays, lists,.. </li></ul><ul><li>Notice that none of the examples on prev slide serve files – they serve typed information. </li></ul><ul><li>Recommendation: Increase Research investments ABOVE the OS level Information Management/Visualization </li></ul>
  9. 9. Summary: It’s the Software… <ul><li>Computing is Information centric </li></ul><ul><li>Scientific computing is Beowulf computing </li></ul><ul><li>Scientific computing becoming Info-centric. </li></ul><ul><li>Adequate investment in files/OS/networking </li></ul><ul><li>Underinvestment in Scientific Information management and visualization tools. </li></ul><ul><li>Computation Grid moves too much data, DataGrid (or App Grid) is right concept </li></ul>
  10. 10. Anecdotal Evidence, Everywhere I go I see Beowulfs <ul><li>Clusters of PCs (or high-slice-price micros) </li></ul><ul><li>True: I have not visited Earth Simulator, but… Google, MSN, Hotmail, Yahoo, NCBI, FNAL, Los Alamos, Cal Tech, MIT, Berkeley, NARO, Smithsonian, Wisconsin, eBay, Amazon.com, Schwab, Citicorp, Beijing, Cern, BaBar, NCSA, Cornell, UCSD, and of course NASA and Cal Tech </li></ul>
  11. 11. Super Computing The Top 10 of Top 500 skip 82 NCAR 3.2 IBM SP2 10 79 HPCx 3.2 IBM SP2 9 76 NOAA 3.3 Intel/HPTi 8 73 CEA 4.0 HP Alpha 7 69 PSC 4.5 HP Alpha 6 64 LLNL 5.7 Intel/NetworX 5 59 LLNL 7.2 IBM ASCI White 4 51 LLNL 7.7 HP ASCI Q 3 44 LLNL 7.7 HP ASCI Q 2 36 Earth Sim Ctr 35.9 NEC Earth-Sim 1 Cumulative TF Site TeraFlops Hardware   Adapted from Top500 Nov 2002
  12. 12. [email_address] The worlds most powerful computer <ul><li>61 TF is sum of top 4 of Top 500. </li></ul><ul><li>61 TF is 9x the number 2 system. </li></ul><ul><li>61 TF more than the sum of systems 2..10 </li></ul>skip [email_address] http://setiathome.ssl.berkeley.edu/totals.html 20 May 2003 5 E+18 FLOPS/day 61.3 TeraFLOPs 3 E+21 ops 3 zeta ops Floating Point Operations 1,514 years 1.5 M years Total CPU time 1,4 M 886 M Results received 1,900 4,493,731 Users Last 24 Hours Total  
  13. 13. And… <ul><li>Google: </li></ul><ul><ul><li>10k cpus, 2PB,… as of 2 years ago </li></ul></ul><ul><ul><li>40 Tops </li></ul></ul><ul><li>AOL, MSN, Hotmail, Yahoo!, … -- all ~10K cpus -- all have ~ 1PB …10PB storage </li></ul><ul><li>Wal-Mart is a PB poster child </li></ul><ul><li>Clusters / Beowulf everywhere you go. </li></ul>skip
  14. 14. Scientific == Beowulf (clusters) <ul><li>Scientific/ Beowulf/ Grid computing 70’s style computing: process / file / socket byte arrays, no data schema or semantics </li></ul><ul><li>batch job scheduling manual parallelism (MPI) poor / no Information management support poor / no Information visualization toolkits </li></ul><ul><li>Recommendation: Increase investment in Info-Management Increase investment in Info-Visualization </li></ul>
  15. 15. Summary: It’s the Software… <ul><li>Computing is Information centric </li></ul><ul><li>Scientific computing is Beowulf computing </li></ul><ul><li>Scientific computing becoming Info-centric. </li></ul><ul><li>Adequate investment in files/OS/networking </li></ul><ul><li>Underinvestment in Scientific Information management and visualization tools. </li></ul><ul><li>Computation Grid moves too much data, DataGrid (or App Grid) is right concept </li></ul>
  16. 16. The Evolution of Science <ul><li>Observational Science </li></ul><ul><ul><li>Scientist gathers data by direct observation </li></ul></ul><ul><ul><li>Scientist analyzes Information </li></ul></ul><ul><li>Analytical Science </li></ul><ul><ul><li>Scientist builds analytical model </li></ul></ul><ul><ul><li>Makes predictions. </li></ul></ul><ul><li>Computational Science </li></ul><ul><ul><li>Simulate analytical model </li></ul></ul><ul><ul><li>Validate model and makes predictions </li></ul></ul><ul><li>Science - Informatics Information Exploration Science Information captured by instruments Or Information generated by simulator </li></ul><ul><ul><li>Processed by software </li></ul></ul><ul><ul><li>Placed in a database / files </li></ul></ul><ul><ul><li>Scientist analyzes database / files </li></ul></ul>
  17. 17. How Discoveries Made? Adapted from slide by George Djorgovski <ul><li>Conceptual Discoveries: e.g., Relativity, QM, Brane World, Inflation … Theoretical, may be inspired by observations </li></ul><ul><li>Phenomenological Discoveries: e.g., Dark Matter, QSOs, GRBs, CMBR, Extrasolar Planets, Obscured Universe … Empirical, inspire theories, can be motivated by them </li></ul>New Technical Capabilities Observational Discoveries Theory <ul><li>Phenomenological Discoveries: </li></ul><ul><li>Explore parameter space </li></ul><ul><li>Make new connections (e.g., multi-  ) Understanding of complex phenomena requires complex, information-rich data (and simulations?) </li></ul>
  18. 18. The Information Avalanche both comp-X and X-info generating Petabytes <ul><li>Comp-Science generating Information avalanche comp-chem, comp-physics, comp-bio, comp-astro, comp-linguistics, comp-music, comp-entertainment, comp-warfare </li></ul><ul><li>Science-Info generating Information avalanche bio-info, astro-info, text-info, </li></ul>
  19. 19. Information Avalanche Stories <ul><li>Turbulence: 100 TB simulation then mine the Information </li></ul><ul><li>BaBar: Grows 1TB/day 2/3 simulation Information 1/3 observational Information </li></ul><ul><li>CERN: LHC will generate 1GB/s 10 PB/y </li></ul><ul><li>VLBA (NRAO) generates 1GB/s today </li></ul><ul><li>NCBI: “only ½ TB” but doubling each year very rich dataset. </li></ul><ul><li>Pixar: 100 TB/Movie </li></ul>
  20. 20. Astro-Info World Wide Telescope http://www.astro.caltech.edu/nvoconf/ http:// www.voforum.org / <ul><li>Premise: Most data is (or could be online) </li></ul><ul><li>Internet is the world’s best telescope: </li></ul><ul><ul><li>It has data on every part of the sky </li></ul></ul><ul><ul><li>In every measured spectral band: optical, x-ray, radio.. </li></ul></ul><ul><ul><li>As deep as the best instruments (2 years ago). </li></ul></ul><ul><ul><li>It is up when you are up. The “seeing” is always great (no working at night, no clouds no moons no..). </li></ul></ul><ul><ul><li>It’s a smart telescope: links objects and data to literature on them. </li></ul></ul>
  21. 21. Why Astronomy Data? <ul><li>It has no commercial value </li></ul><ul><ul><li>No privacy concerns </li></ul></ul><ul><ul><li>Can freely share results with others </li></ul></ul><ul><ul><li>Great for experimenting with algorithms </li></ul></ul><ul><li>It is real and well documented </li></ul><ul><ul><li>High-dimensional data (with confidence intervals) </li></ul></ul><ul><ul><li>Spatial data </li></ul></ul><ul><ul><li>Temporal data </li></ul></ul><ul><li>Many different instruments from many different places and many different times </li></ul><ul><li>But, it’s the same universe so comparisons make sense & are interesting. </li></ul><ul><li>Federation is a goal </li></ul><ul><li>There is a lot of it (petabytes) </li></ul><ul><li>Great sandbox for data mining algorithms </li></ul><ul><ul><li>Can share cross company </li></ul></ul><ul><ul><li>University researchers </li></ul></ul><ul><li>Great way to teach both Astronomy and Computational Science </li></ul>IRAS 100  ROSAT ~keV DSS Optical 2MASS 2  IRAS 25  NVSS 20cm WENSS 92cm GB 6cm
  22. 22. Summary: It’s the Software… <ul><li>Computing is Information centric </li></ul><ul><li>Scientific computing is Beowulf computing </li></ul><ul><li>Scientific computing becoming Info-centric. </li></ul><ul><li>Adequate investment in files/OS/networking </li></ul><ul><li>Underinvestment in Scientific Information management and visualization tools. </li></ul><ul><li>Computation Grid moves too much data, DataGrid (or App Grid) is right concept </li></ul>
  23. 23. What X-info Needs from us (cs) (not drawn to scale) Science Data & Questions Scientists Database To store data Execute Queries Plumbers Data Mining Algorithms Miners Question & Answer Visualization Tools
  24. 24. Data Access is hitting a wall FTP and GREP are not adequate <ul><li>You can GREP 1 MB in a second </li></ul><ul><li>You can GREP 1 GB in a minute </li></ul><ul><li>You can GREP 1 TB in 2 days </li></ul><ul><li>You can GREP 1 PB in 3 years. </li></ul><ul><li>Oh!, and 1PB ~5,000 disks </li></ul><ul><li>At some point you need indices to limit search parallel data search and analysis </li></ul><ul><li>This is where databases can help </li></ul><ul><li>You can FTP 1 MB in 1 sec </li></ul><ul><li>You can FTP 1 GB / min (= 1 $/GB) </li></ul><ul><li>… 2 days and 1K$ </li></ul><ul><li>… 3 years and 1M$ </li></ul>
  25. 25. Next-Generation Data Analysis <ul><li>Looking for </li></ul><ul><ul><li>Needles in haystacks – the Higgs particle </li></ul></ul><ul><ul><li>Haystacks: Dark matter, Dark energy </li></ul></ul><ul><li>Needles are easier than haystacks </li></ul><ul><li>Global statistics have poor scaling </li></ul><ul><ul><li>Correlation functions are N 2 , likelihood techniques N 3 </li></ul></ul><ul><li>As data and processing grow at same rate, we can only keep up with N logN </li></ul><ul><li>A way out? </li></ul><ul><ul><li>Discard notion of optimal (data is fuzzy, answers are approximate) </li></ul></ul><ul><ul><li>Don’t assume infinite computational resources or memory </li></ul></ul><ul><li>Requires combination of statistics & computer science </li></ul><ul><li>Recommendation: invest in data mining research both general and domain-specific. </li></ul>
  26. 26. Analysis and Databases <ul><li>Statistical analysis deals with </li></ul><ul><ul><li>Creating uniform samples </li></ul></ul><ul><ul><li>data filtering & censoring bad data </li></ul></ul><ul><ul><li>Assembling subsets </li></ul></ul><ul><ul><li>Estimating completeness </li></ul></ul><ul><ul><li>Counting and building histograms </li></ul></ul><ul><ul><li>Generating Monte-Carlo subsets </li></ul></ul><ul><ul><li>Likelihood calculations </li></ul></ul><ul><ul><li>Hypothesis testing </li></ul></ul><ul><li>Traditionally these are performed on files </li></ul><ul><li>Most of these tasks are much better done inside a database close to the data . </li></ul><ul><li>Move Mohamed to the mountain, not the mountain to Mohamed. </li></ul><ul><li>Recommendation: Invest in database research: extensible databases: text, temporal, spatial, … data interchange, parallelism, indexing, query optimization </li></ul>
  27. 27. Goal: Easy Data Publication & Access <ul><li>Augment FTP with data query: Return intelligent data subsets </li></ul><ul><li>Make it easy to </li></ul><ul><ul><li>Publish: Record structured data </li></ul></ul><ul><ul><li>Find: </li></ul></ul><ul><ul><ul><li>Find data anywhere in the network </li></ul></ul></ul><ul><ul><ul><li>Get the subset you need </li></ul></ul></ul><ul><ul><li>Explore datasets interactively </li></ul></ul><ul><li>Realistic goal: </li></ul><ul><ul><li>Make it as easy as publishing/reading web sites today . </li></ul></ul>
  28. 28. Data Federations of Web Services <ul><li>Massive datasets live near their owners: </li></ul><ul><ul><li>Near the instrument’s software pipeline </li></ul></ul><ul><ul><li>Near the applications </li></ul></ul><ul><ul><li>Near data knowledge and curation </li></ul></ul><ul><ul><li>Super Computer centers become Super Data Centers </li></ul></ul><ul><li>Each Archive publishes a web service </li></ul><ul><ul><li>Schema: documents the data </li></ul></ul><ul><ul><li>Methods on objects (queries) </li></ul></ul><ul><li>Scientists get “personalized” extracts </li></ul><ul><li>Uniform access to multiple Archives </li></ul><ul><ul><li>A common global schema </li></ul></ul>Federation
  29. 29. Web Services: The Key? <ul><li>Web SERVER: </li></ul><ul><ul><li>Given a url + parameters </li></ul></ul><ul><ul><li>Returns a web page (often dynamic) </li></ul></ul><ul><li>Web SERVICE: </li></ul><ul><ul><li>Given a XML document (soap msg) </li></ul></ul><ul><ul><li>Returns an XML document </li></ul></ul><ul><ul><li>Tools make this look like an RPC. </li></ul></ul><ul><ul><ul><li>F(x,y,z) returns (u, v, w) </li></ul></ul></ul><ul><ul><li>Distributed objects for the web. </li></ul></ul><ul><ul><li>+ naming, discovery, security,.. </li></ul></ul><ul><li>Internet-scale distributed computing </li></ul>Your program Data In your address space Web Service soap object in xml Your program Web Server http Web page
  30. 30. The Challenge <ul><li>This has failed several times before– understand why. </li></ul><ul><li>Develop </li></ul><ul><ul><li>Common data models (schemas), </li></ul></ul><ul><ul><li>Common interfaces (class/method) </li></ul></ul><ul><li>Build useful prototypes (nodes and portals) </li></ul><ul><li>Create a community that uses the prototypes and evolves the prototypes. </li></ul>
  31. 31. Grid and Web Services Synergy <ul><li>I believe the Grid will be many web services </li></ul><ul><li>IETF standards Provide </li></ul><ul><ul><li>Naming </li></ul></ul><ul><ul><li>Authorization / Security / Privacy </li></ul></ul><ul><ul><li>Distributed Objects </li></ul></ul><ul><ul><ul><li>Discovery, Definition, Invocation, Object Model </li></ul></ul></ul><ul><ul><li>Higher level services: workflow, transactions, DB,.. </li></ul></ul><ul><li>Synergy: commercial Internet & Grid tools </li></ul>
  32. 32. Summary: It’s the Software… <ul><li>Computing is Information centric </li></ul><ul><li>Scientific computing is Beowulf computing </li></ul><ul><li>Scientific computing becoming Info-centric. </li></ul><ul><li>Adequate investment in files/OS/networking </li></ul><ul><li>Underinvestment in Scientific Information management and visualization tools. </li></ul><ul><li>Computation Grid moves too much data, DataGrid (or App Grid) is right concept </li></ul>
  33. 33. Recommendations <ul><li>Increase Research investments ABOVE the OS level Information Management/Visualization </li></ul><ul><li>Invest in database research: extensible databases: text, temporal, spatial, … data interchange, parallelism, indexing, query optimization </li></ul><ul><li>invest in data mining research both general and domain-specific </li></ul>
  34. 34. Stop Here <ul><li>Bonus slides on Distributed Computing Economics </li></ul>
  35. 35. Distributed Computing Economics <ul><li>Why is Seti@Home a great idea </li></ul><ul><li>Why is Napster a great deal? </li></ul><ul><li>Why is the Computational Grid uneconomic </li></ul><ul><li>When does computing on demand work? </li></ul><ul><li>What is the “right” level of abstraction </li></ul><ul><li>Is the Access Grid the real killer app? </li></ul>Based on: Distributed Computing Economics, Jim Gray, Microsoft Tech report, March 2003, MSR-TR-2003-24 http://research.microsoft.com/research/pubs/view.aspx?tr_id=655
  36. 36. Computing is Free <ul><li>Computers cost 1k$ (if you shop right) </li></ul><ul><li>So 1 cpu day == 1$ </li></ul><ul><li>If you pay the phone bill (and I do) Internet bandwidth costs 50 … 500$/mbps/m (not including routers and management). </li></ul><ul><li>So 1GB costs 1$ to send and 1$ to receive </li></ul>
  37. 37. Why is Seti@Home a Good Deal? <ul><li>Send 300 KB for costs 3e-4$ </li></ul><ul><li>User computes for ½ day: benefit .5e-1$ </li></ul><ul><li>ROI: 1500:1 </li></ul>
  38. 38. Why is Napster a Good Deal? <ul><li>Send 5 MB costs 5e-3$ </li></ul><ul><li>½ a penny per song </li></ul><ul><li>Both sender and receiver can afford it. </li></ul><ul><li>Same logic powers web sites (Yahoo!...): </li></ul><ul><ul><li>1e-3$/page view advertising revenue </li></ul></ul><ul><ul><li>1e-5$/page view cost of serving web page </li></ul></ul><ul><ul><li>100:1 ROI </li></ul></ul>
  39. 39. The Cost of Computing: Computers are NOT free! <ul><li>Capital Cost of a TpcC system is mostly storage and storage software (database) </li></ul><ul><li>IBM 32 cpu, 512 GB ram 2,500 disks, 43 TB (680,613 tpmC @ 11.13 $/tpmc available 11/08/03) http://www.tpc.org/results/individual_results/IBM/IBMp690es_05092003.pdf </li></ul><ul><li>A 7.5M$ super-computer </li></ul><ul><li>Total Data Center Cost: 40% capital &facilities 60% staff (includes app development) </li></ul>
  40. 40. Computing Equivalents 1 $ buys <ul><li>1 day of cpu time </li></ul><ul><li>4 GB ram for a day </li></ul><ul><li>1 GB of network bandwidth </li></ul><ul><li>1 GB of disk storage </li></ul><ul><li>10 M database accesses </li></ul><ul><li>10 TB of disk access (sequential) </li></ul><ul><li>10 TB of LAN bandwidth (bulk) </li></ul>
  41. 41. Some consequences <ul><li>Beowulf networking is 10,000x cheaper than WAN networking factors of 10 5 matter. </li></ul><ul><li>The cheapest and fastest way to move a Terabyte cross country is sneakernet. 24 hours = 4 MB/s 50$ shipping vs 1,000$ wan cost. </li></ul><ul><li>Sending 10PB CERN data via network is silly: buy disk bricks in Geneva, fill them, ship them. </li></ul>TeraScale SneakerNet: Using Inexpensive Disks for Backup, Archiving, and Data Exchange Jim Gray; Wyman Chong; Tom Barclay; Alex Szalay; Jan vandenBerg Microsoft Technical Report may 2002, MSR-TR-2002-54 http://research.microsoft.com/research/pubs/view.aspx?tr_id=569
  42. 42. How Do You Move A Terabyte? Source: TeraScale Sneakernet, Microsoft Research, Jim Gray et. all 14 minutes 617 200 1,920,000 9600 OC 192 2.2 hours 1000 Gbps 1 day 100 100 Mpbs 14 hours 976 316 49,000 155 OC3 2 days 2,010 651 28,000 43 T3 2 months 2,469 800 1,200 1.5 T1 5 months 360 117 70 0.6 Home DSL 6 years 3,086 1,000 40 0.04 Home phone Time/TB $/TB Sent $/Mbps Rent $/month Speed Mbps Context
  43. 43. Computational Grid Economics <ul><li>To the extent that computational grid is like Seti@Home or ZetaNet or Folding@home or… it is a great thing </li></ul><ul><li>The extent that the computational grid is MPI or data analysis, it fails on economic grounds: move the programs to the data, not the data to the programs. </li></ul><ul><li>The Internet is NOT the cpu backplane. </li></ul><ul><li>The USG should not hide this economic fact from the academic/scientific research community. </li></ul>
  44. 44. Computing on Demand <ul><li>Was called outsourcing / service bureaus in my youth. CSC and IBM did it. </li></ul><ul><li>It is not a new way of doing things: think payroll. Payroll is standard outsource. </li></ul><ul><li>Now we have Hotmail, Salesforce.com, Oracle.com,…. </li></ul><ul><li>Works for standard apps. </li></ul><ul><li>Airlines outsource reservations. Banks outsource ATMs. </li></ul><ul><li>But Amazon, Amex, Wal-Mart, ... Can’t outsource their core competence. </li></ul><ul><li>So, COD works for commoditized services. </li></ul>
  45. 45. What’s the right abstraction level for Internet Scale Distributed Computing? <ul><li>Disk block? No too low. </li></ul><ul><li>File? No too low. </li></ul><ul><li>Database? No too low. </li></ul><ul><li>Application? Yes, of course. </li></ul><ul><ul><li>Blast search </li></ul></ul><ul><ul><li>Google search </li></ul></ul><ul><ul><li>Send/Get eMail </li></ul></ul><ul><ul><li>Portals that federate astronomy archives ( http://skyQuery.Net/ ) </li></ul></ul><ul><li>Web Services (.NET, EJB, OGSA) give this abstraction level. </li></ul>
  46. 46. Access Grid <ul><li>Q: What comes after the telephone? </li></ul><ul><li>A: eMail? </li></ul><ul><li>A: Instant messaging? </li></ul><ul><li>Both seem retro technology: text & emotons. </li></ul><ul><li>Access Grid could revolutionize human communication. </li></ul><ul><li>But, it needs a new idea. </li></ul><ul><li>Q: What comes after the telephone? </li></ul>
  47. 47. Distributed Computing Economics <ul><li>Why is Seti@Home a great idea? </li></ul><ul><li>Why is Napster a great deal? </li></ul><ul><li>Why is the Computational Grid uneconomic </li></ul><ul><li>When does computing on demand work? </li></ul><ul><li>What is the “right” level of abstraction? </li></ul><ul><li>Is the Access Grid the real killer app? </li></ul>Based on: Distributed Computing Economics, Jim Gray, Microsoft Tech report, March 2003, MSR-TR-2003-24 http://research.microsoft.com/research/pubs/view.aspx?tr_id=655
  48. 48. Turbulence, an old problem Observational Described 5 centuries ago by Leonardo <ul><li>Theoretical </li></ul><ul><li>Best minds have tried and …. “moved on ”: </li></ul><ul><li>Lamb: … “When I die and go to heaven…” </li></ul><ul><li>Heisenberg, von Weizsäcker …some attempts </li></ul><ul><li>Partial successes: Kolmogorov, Onsager </li></ul><ul><li>Feynman “…the last unsolved problem of classical physics” </li></ul>Adapted from ASCI ASCP gallery http://www.cacr.caltech.edu/~slombey/asci/fluids/turbulence-volren.med.jpg
  49. 49. <ul><li>How does the turbulent energy cascade work? </li></ul><ul><li>Direct numerical simulation of “turbulence in a box” </li></ul><ul><li>Pushing comp-limits along specific directions: </li></ul>Simulation: Comp-Physics Ref: Cao, Chen et al. Ref: Chen & Kraichnan <ul><ul><li>Three-dimensional (512 3 - 4,096 3 ), </li></ul></ul><ul><ul><li>but only static information </li></ul></ul><ul><ul><li>8192 2 , but only two-dimensional </li></ul></ul>Slide courtesy of Charles Meneveau @ JHU
  50. 50. <ul><li>We can now “put it all together”: </li></ul><ul><li>Large scale range, scale-ratio O(1,000) </li></ul><ul><li>Three-dimensional in space </li></ul><ul><li>Time-evolution and Lagrangian approach (follow the flow) </li></ul><ul><li>Turbulence data-base: </li></ul><ul><li>Create a 100 TB database of O(2,000) consecutive snapshots of a 1,024 3 turbulence simulation. </li></ul><ul><li>Mine the database to understand flows in detail </li></ul>Data-Exploration: Physics-Info Slide courtesy of Charles Meneveau, Alex Szalay @ JHU
  51. 51. Following 18 slides from 1997 <ul><li>Bell & Gray Computer Industry “laws” </li></ul><ul><li>Rules of thumb </li></ul><ul><li>Still relevant </li></ul>
  52. 52. Computer Industry Laws (rules of thumb) <ul><li>Metcalf’s law </li></ul><ul><li>Moore’s First Law </li></ul><ul><li>Bell’s Computer Classes (7 price tiers) </li></ul><ul><li>Bell’s Platform Evolution </li></ul><ul><li>Bell’s Platform Economics </li></ul><ul><li>Bill’s Law </li></ul><ul><li>Software Economics </li></ul><ul><li>Grove’s law </li></ul><ul><li>Moore’s second law </li></ul><ul><li>Is Info-Demand Infinite? </li></ul><ul><li>The Death of Grosch’s Law </li></ul>
  53. 53. Metcalf’s Law Network Utility = Users 2 <ul><li>How many connections can it make? </li></ul><ul><ul><li>1 user: no utility </li></ul></ul><ul><ul><li>1K users: a few contacts </li></ul></ul><ul><ul><li>1M users: many on net </li></ul></ul><ul><ul><li>1B users: everyone on net </li></ul></ul><ul><li>That is why the Internet is so “hot” </li></ul><ul><ul><li>Exponential benefit </li></ul></ul>
  54. 54. Moore’s First Law <ul><li>XXX doubles every 18 months 60% increase per year </li></ul><ul><ul><li>Micro Processor speeds </li></ul></ul><ul><ul><li>chip density </li></ul></ul><ul><ul><li>Magnetic disk density </li></ul></ul><ul><ul><li>Communications bandwidth WAN bandwidth approaching LANs </li></ul></ul><ul><li>Exponential Growth: </li></ul><ul><ul><li>The past does not matter </li></ul></ul><ul><ul><li>10x here, 10x there, soon you're talking REAL change. </li></ul></ul><ul><li>PC costs decline faster than any other platform </li></ul><ul><ul><li>Volume & learning curves </li></ul></ul><ul><ul><li>PCs will be the building bricks of all future systems </li></ul></ul>128KB 128MB 2000 8KB 1MB 8MB 1GB 1970 1980 1990 1M 16M bits: 1K 4K 16K 64K 256K 4M 64M 256M 1 chip memory size ( 2 MB to 32 MB)
  55. 55. Bumps in the Moore’s Law Road <ul><li>DRAM: </li></ul><ul><ul><li>1988: US Anti-Dumping rules </li></ul></ul><ul><ul><li>1993-1995: ?? price flat Magnetic Disk </li></ul></ul><ul><ul><li>1965-1989: 10x/decade </li></ul></ul><ul><ul><li>1989-2002: 7x/3year! 1,000X/decade </li></ul></ul>.01 1 100 10,000 1970 1980 1990 2000 $/MB of DISK 1 100 10000 1000000 1970 1980 1990 2000 $/MB of DRAM
  56. 56. Gordon Bell’s 1975 VAX planning model... He didn’t believe it! System Price = 5 x 3 x .04 x memory size/ 1.26 (t-1972) K$ 5x: Memory is 20% of cost 3x:DEC markup .04x: $ per byte He didn’t believe: The projection 500$ machine He couldn’t comprehend implications
  57. 57. Gordon Bell’s Processing, memories, & comm 100 years
  58. 58. Gordon Bell’s Seven Price Tiers <ul><li>10$: wrist watch computers </li></ul><ul><li>100$: pocket/ palm computers </li></ul><ul><li>1,000$: portable computers </li></ul><ul><li>10,000$: personal computers (desktop) </li></ul><ul><li>100,000$: departmental computers (closet) </li></ul><ul><li>1,000,000$: site computers (glass house) </li></ul><ul><li>10,000,000$:regional computers (glass castle) </li></ul>SuperServer: Costs more than 100,000 $ “ Mainframe” Costs more than 1M$ Must be an array of processors, disks, tapes comm ports
  59. 59. Bell’s Evolution of Computer Classes Technology enable two evolutionary paths: 1. constant performance, decreasing cost 2. constant price, increasing performance 1.26 = 2x/3 yrs -- 10x/decade; 1/1.26 = .8 1.6 = 4x/3 yrs --100x/decade; 1/1.6 = .62 ?? Time Mainframes (central) Minis (dep’t.) PCs (personals) Log Price WSs
  60. 60. Gordon Bell’s Platform Economics <ul><li>Traditional computers: Custom or Semi-Custom high-tech and high-touch </li></ul><ul><li>New computers: high-tech and no-touch </li></ul>Computer type $ units Mainframe WS Browser 0.01 0.1 1 10 100 1000 10000 100000 Mainframe WS Browser Price (K$) Volume (K) App price
  61. 61. Software Economics CIRCA 1997 <ul><li>An engineer costs about 150 k$/year </li></ul><ul><li>R&D gets [5%…15%] of budget </li></ul><ul><li>Need [3M$…1M$] revenue per engineer </li></ul>Microsoft: 9 B$ R&D 16% SG&A 34% Product&Service 13% Tax 13% Profit 24% Intel 16 B$ R&D 8% SG&A 11% Product&Service 47% Tax 12% Profit 22% R&D 8% SG&A 22% Product&Service 59% Tax 5% Profit 6% IBM: 72 B$ R&D 9% SG&A 43% Tax 7% Profit 15% Product& Services 26% Oracle: 3 B$
  62. 62. Software Economics: Bill’s Law <ul><li>Bill Joy’s law (Sun ): Don’t write software for less than 100,000 platforms . @10M$ engineering expense, 1,000$ price </li></ul><ul><li>Bill Gate’s law : Don’t write software for less than 1,000,000 platforms . @10M$ engineering expense, 100$ price </li></ul><ul><li>Examples: </li></ul><ul><ul><li>UNIX vs NT: 3,500$ vs 500$ </li></ul></ul><ul><ul><li>Oracle vs SQL-Server: 100,000$ vs 6,000$ </li></ul></ul><ul><ul><li>No Spreadsheet or Presentation pack on UNIX/VMS/... </li></ul></ul><ul><li>Commoditization of base Software & Hardware </li></ul>
  63. 63. Grove's Law The New Computer Industry <ul><li>Horizontal integration is new structure </li></ul><ul><li>Each layer picks best from lower layer. </li></ul><ul><li>Desktop (C/S) market </li></ul><ul><ul><li>1991: 50% </li></ul></ul><ul><ul><li>1995: 75% </li></ul></ul>Intel & Seagate Silicon & Oxide Systems Baseware Middleware Applications SAP Oracle Microsoft Compaq Integration EDS Operation AT&T Function Example
  64. 64. Moore’s Second Law <ul><li>The Cost of Fab Lines Doubles Every Generation (3 years) </li></ul><ul><li>Money Limit: hard to imagine 10 B$ line 20 B$ line 40 B$ line </li></ul><ul><li>Physical limit: </li></ul><ul><ul><li>Quantum Effects at 0.25 micron now 0.05 micron seems hard 12 years, 3 generations </li></ul></ul><ul><ul><li>Lithograph: need Xray below 0.13 micron </li></ul></ul>$1 $10 $100 $1,000 $10,000 1960 1970 1980 1990 2000 Year M$ / Fab Line
  65. 65. Constant Dollars vs Constant Work <ul><li>Constant Work : </li></ul><ul><ul><li>One SuperServer can do all the world’s computations . </li></ul></ul><ul><li>Constant Dollars : </li></ul><ul><ul><li>The world spends 10% on information processing </li></ul></ul><ul><ul><li>Computers are moving from 5% penetration to 50% </li></ul></ul><ul><ul><ul><li>300 B$ to 3T$ </li></ul></ul></ul><ul><ul><ul><li>We have the patent on the byte and algorithm </li></ul></ul></ul>
  66. 66. Crossing the Chasm Old Market Old Technology New Technology Very Hard hard Boring Competitve Slow Growth No Product No Customers product finds customers Customers find product hard New Market
  67. 67. Billions of Clients Need Millions of Servers mobile clients fixed clients server super server Clients Servers Super Servers Large Databases High Traffic shared data <ul><li>All clients are networked to servers </li></ul><ul><ul><ul><li>may be nomadic or on-demand </li></ul></ul></ul><ul><li>Fast clients want faster servers </li></ul><ul><li>Servers provide </li></ul><ul><ul><li>data, </li></ul></ul><ul><ul><li>control, </li></ul></ul><ul><ul><li>coordination </li></ul></ul><ul><ul><li>communication </li></ul></ul>
  68. 68. The Parallel Law of Computing <ul><li>Grosch's Law: </li></ul><ul><li>Parallel Law: </li></ul><ul><li>Needs </li></ul><ul><ul><li>Linear Speedup and Linear Scaleup </li></ul></ul><ul><ul><li>Not always possible </li></ul></ul>1 MIPS 1 $ 1,000 $ 1,000 MIPS 2x $ is 2x performance 1 MIPS 1 $ 1,000 MIPS 32 $ .03$/MIPS 2x $ is 4x performance
  69. 69. Our Biggest Problem What is the trend line? This wasn’t a problem when MIPS cost 100k$ and Disks cost 1k$/MB

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