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Running Hot October 2008


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I gave this talk at a conference for young scientists in New Zealand, "Running Hot": It was a great meeting. My slides are mostly images, so may not make too much sense.

Abstract follows: Impressed with the telephone, Arthur Mee predicted in 1898 that if videoconferencing could be developed, ‘earth will be in truth a paradise.’ Since his time, rapid technological change, in particular in telecommunications, has transformed the scientific playing field in ways that while not entirely paradisical, certainly have profound implications for New Zealand scientists. The Internet has abolished distance, as Mee also predicted–a New Zealand scientist can participate as fully in online discussions as anyone else, and their blog can be every bit as influential. Exponential improvements in networks, computing, sensors, and data storage are also profoundly transforming the practice of science in many disciplines. But those seeking to leverage these advances become painfully familiar with the ‘dirty underbelly’ of exponentials: if you don’t constantly innovate, you can fall behind exponentially fast. Such considerations pose big challenges for the individual scientist and for institutions, for researchers and educators, and for research funders. Some of the old ways of researching and educating need to be preserved, others need to be replaced to take advantage of new methods. But what should we preserve? What should we seek to change?

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Running Hot October 2008

  1. 1. Research in Paradise Ian Foster Computation Institute Argonne National Lab & University of Chicago
  2. 3. Earth to be paradise; distance to lose enchantment <ul><li>“ If, as it is said to be not unlikely in the near future, the principle of sight is applied to the telephone as well as that of sound, earth will be in truth a paradise, and distance will lose its enchantment by being abolished altogether.” </li></ul><ul><li>— Arthur Mee, 1898 </li></ul>
  3. 4. Knowledge arises from experience
  4. 5. Isaac Newton, 1687 “ I can calculate the motion of heavenly bodies, but not the madness of people.”
  5. 6. Computing as a profession
  6. 7. Peak performance (floating point ops/sec) Argonne 1940 1950 1960 1970 1980 1990 2000 2010 Year Introduced 1E+2 1E+5 1E+8 1E+11 1E+14 1E+17 Peak Speed (flops) Doubling time = 1.5 yr. ENIAC (vacuum tubes) UNIVAC IBM 701 IBM 704 IBM 7090 (transistors) IBM Stretch CDC 6600 (ICs) CDC 7600 CDC STAR-100 (vectors) CRAY-1 Cyber 205 X-MP2 (parallel vectors) CRAY-2 X-MP4 Y-MP8 i860 (MPPs) ASCI White, ASCI Q Petaflop Blue Gene/L Blue Pacific Delta CM-5 Paragon NWT ASCI Red Option ASCI Red CP-PACS Earth VP2600/10 SX-3/44 Red Storm ILLIAC IV SX-2 SX-4 SX-5 S-810/20 T3D T3E multi-Petaflop Thunder
  7. 8. Exponentials are funny things Obliviousness Something’s happening Shock
  8. 9. Type Ia Supernova : SN 1994D
  9. 10. Don Lamb et al., FLASH Center, University of Chicago
  10. 11. Don Lamb et al., FLASH Center, University of Chicago
  11. 12. Don Lamb et al., FLASH Center, University of Chicago
  12. 13. Don Lamb et al., FLASH Center, University of Chicago
  13. 14. Don Lamb et al., FLASH Center, University of Chicago
  14. 15. System-level science National Center for Atmospheric Research
  15. 16. The data deluge
  16. 17. Growth of Genbank (1982-2005) Broad Institute
  17. 18. More data does not always mean more knowledge Folker Meyer, Genome Sequencing vs. Moore’s Law: Cyber Challenges for the Next Decade, CTWatch , August 2006.
  18. 20. <ul><li>Proteomics </li></ul><ul><li>Genomics </li></ul><ul><li>Transcriptomics </li></ul><ul><li>Protein sequence prediction </li></ul><ul><li>Phenotypic studies </li></ul><ul><li>Phylogeny </li></ul><ul><li>Sequence analysis </li></ul><ul><li>Protein structure prediction </li></ul><ul><li>Protein-protein interaction </li></ul><ul><li>Metabolomics </li></ul><ul><li>Model organism collections </li></ul><ul><li>Systems biology </li></ul><ul><li>Health epidemiology </li></ul><ul><li>Organisms </li></ul><ul><li>Disease …. </li></ul>1070 molecular bio databases Nucleic Acids Research Jan 2008 (96 in Jan 2001) Slide: Carole Goble
  19. 21. The dirty underbelly of exponentials My relative capability if I do nothing Capability at constant investment Log
  20. 22. The Red Queen’s race <ul><li>&quot;Well, in our country,&quot; said Alice … &quot;you'd generally get to somewhere else — if you run very fast for a long time, as we've been doing.” </li></ul><ul><li>&quot;A slow sort of country!&quot; said the Queen. &quot;Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!&quot; </li></ul>
  21. 23. Kiwi Advanced Research and Education Network
  22. 24. An American philosopher speaks on the value of KAREN <ul><li>“ 80 percent of success is showing up” </li></ul>Woody Allen
  23. 25. Services for science <ul><li>We expose data and software as services … </li></ul><ul><li>which others discover , decide to use, … </li></ul><ul><li>and compose to create new functions ... </li></ul><ul><li>which they publish as new services. </li></ul><ul><li>Technical … </li></ul><ul><li>Modeling </li></ul><ul><li>Authoring </li></ul><ul><li>Semantics </li></ul><ul><li>Discovery </li></ul><ul><li>socio-technical challenges </li></ul><ul><li>Incentives </li></ul><ul><li>Policy, trust </li></ul><ul><li>Reproducibility </li></ul><ul><li>Life cycle </li></ul>“ Service-Oriented Science”, Science , 2005 and
  24. 26. The cancer Biomedical Informatics Grid Globus
  25. 27. As of Sept 18, 2008: 122 participants 81 services 62 data 19 analytical
  26. 28. As of Oct 19 , 2008: 122 participants 105 services 70 data 35 analytical
  27. 30. Image: Andrey Rzhetsky
  28. 32. New Ways of Knowing 300 BCE 1700 1950 1990 Multiplied by the power of collaboration … & exponentials Empiricism Data Theory Simulation
  29. 33. Thank You! Computation Institute