The document discusses how computation can accelerate the generation of new knowledge by enabling large-scale collaborative research and extracting insights from vast amounts of data. It provides examples from astronomy, physics simulations, and biomedical research where computation has allowed more data and researchers to be incorporated, advancing various fields more quickly over time. Computation allows for data sharing, analysis, and hypothesis generation at scales not previously possible.

1. Computation and Knowledge Ian Foster Computation Institute Argonne National Lab & University of Chicago

2. Abstract I speak to the question of how computation can contribute to the generation of new knowledge by accelerating the work of distributed collaborative teams and enabling the extraction of knowledge from large quantities of information produced by many workers. I illustrate my presentation with examples of work being performed within the Computation Institute at the University of Chicago and Argonne National Laboratory.

4. Knowledge Generation in Astronomy ~1600 30 years ? years 10 years 6 years 2 years

5. Astronomy, from 1600 to 2000 Automation 10 -1  10 8 Hz data capture Community 10 0  10 4 astronomers (10 6 amateur) Computation Data 10 6  10 15 B aggregate 10 -1  10 15 Hz peak Literature 10 1  10 5 pages/year

6. Astronomy, from 1600 to 2000 Automation 10 -1  10 8 Hz data capture Community 10 0  10 4 astronomers (10 6 amateur) Computation Data 10 6  10 15 B aggregate 10 -1  10 15 Hz peak Literature 10 1  10 5 pages/year Text mining Federation/ collaboration Data analysis Complex system modeling Hypothesis generation Experiment design

7. FLASH Turbulence Simulation (R.Fisher, D.Lamb, et al.) LLNL BG/L External users access processed dataset 74 million files 154 TB 1 week, 65K CPUs 11M CPU hours Largest compressible homogeneous isotropic turbulence simulation 23 TB 3 weeks @ 20 MB/sec (GridFTP) Globus

8. An End-to-End Systems Problem That Includes … Massively parallel simulation High-performance parallel I/O Remote visualization High-speed reliable data delivery High-performance local analysis Data access and analysis by external users Troubleshooting Security Orchestration of distributed activities

9. Data Delivery as a Systems Problem Data complexity Many components Parallelism (in many places) Network heterogeneities (e.g., firewalls) Space (or the lack of it) Protocols Failures at many levels Deadlines Resource contention Policy and priorities 74 million files 154 TB 2 weeks @20 MB/sec 3 hours @2000 MB/sec 2 mins? @200,000 MB/sec 23 TB

10. Send 1 GB partitioned into equi-sized files over 60 ms RTT, 1 Gbit/s WAN Megabit/sec File size (Kbyte) (16MB TCP buffer) Number of files John Bresnahan et al., Argonne Globus

11. LIGO Gravitational Wave Observatory Birmingham • >1 Terabyte/day to 8 sites 770 TB replicated to date: >120 million replicas MTBF = 1 month Ann Chervenak et al., ISI; Scott Koranda et al, LIGO Cardiff AEI/Golm Globus

12. Lag Plot for Data Transfers to Caltech Credit: Kevin Flasch, LIGO

15. Cancer Biology Globus

16. Service-Oriented Science People create services (data, code, instr.) … which I discover (& decide whether to use) … & compose to create a new function ... & then publish as a new service.  I find “someone else” to host services, so I don’t have to become an expert in operating services & computers!  I hope that this “someone else” can manage security, reliability, scalability, … ! ! “ Service-Oriented Science”, Science , 2005

17. Discovering Services Assume success Syntax, semantics Permissions Reputation  The ultimate arbiter?  Types, ontologies  Can I use it?  Billions of services A B

18. Discovery (1): Registries Duke OSU NCI NCI Globus

19. Discovery (2): Standardized Vocabularies Core Services Grid Service Uses Terminology Described In Cancer Data Standards Repository Enterprise Vocabulary Services References Objects Defined in Service Metadata Publishes Subscribes to and Aggregates Queries Service Metadata Aggregated In Registers To Discovery Client API Index Service Globus

21. Text Mining

22. More Knowledge (?) US papers/year in ApJ+AJ+PASP

23. GeneWays Online Journals Pathways GeneWays Andrey Rzhetsky et al. Screening 250,000 journal articles 2.5M reasoning chains 4M statements

24. Image: Andrey Rzhetsky

26. Image: Andrey Rzhetsky

27. Image: Andrey Rzhetsky

28. Image: Andrey Rzhetsky

29. Evidence Integration: Genetics & Disease Susceptibility Identify Genes Phenotype 1 Phenotype 2 Phenotype 3 Phenotype 4 Predictive Disease Susceptibility Physiology Metabolism Endocrine Proteome Immune Transcriptome Biomarker Signatures Morphometrics Pharmacokinetics Ethnicity Environment Age Gender Source: Terry Magnuson

31. The Data Deluge

32. Images courtesy Mark Ellisman, UCSD

33. Images courtesy Mark Ellisman, UCSD

34. Images courtesy Mark Ellisman, UCSD

35. Images courtesy Mark Ellisman, UCSD A human brain @ 1 micron voxel = 3.5 peta (10 15 )bytes

36. Understanding increases far more slowly Methodological bottlenecks?  Improved technology Human limitations?  Computer-assisted discovery

37. Data Analysis gets Fuzzy Global statistics? Correlation functions: N 2 Likelihood methods: N 3 Best we can do is N or maybe N logN (scale approximate) Haystack: Jim Gray/Alex Szalay

38. Towards an Open Analytics Environment Data in “ No limits” Storage Computing Format Program Allowing for Provenance Collaboration Annotation Results out Programs & rules in

39. Tagging & Social Networking GLOSS : Generalized Labels Over Scientific data Sources (Foster, Nestorov)

40. High-Performance Data Analytics Functional MRI Ben Clifford, Mihael Hatigan, Mike Wilde, Yong Zhao Globus

41. Many Tasks: Identifying Potential Drug Targets 2M+ ligands Protein x target(s) (Mike Kubal, Benoit Roux, and others)

42. start report DOCK6 Receptor (1 per protein: defines pocket to bind to) ZINC 3-D structures ligands complexes NAB script parameters (defines flexible residues, #MDsteps) Amber Score: 1. AmberizeLigand 3. AmberizeComplex 5. RunNABScript end BuildNABScript NAB Script NAB Script Template Amber prep: 2. AmberizeReceptor 4. perl: gen nabscript FRED Receptor (1 per protein: defines pocket to bind to) Manually prep DOCK6 rec file Manually prep FRED rec file 1 protein (1MB) PDB protein descriptions 4 million tasks 500K cpu-hrs (Mike Kubal, Benoit Roux, and others) 6 GB 2M structures (6 GB) DOCK6 FRED ~4M x 60s x 1 cpu ~60K cpu-hrs Amber ~10K x 20m x 1 cpu ~3K cpu-hrs Select best ~500 ~500 x 10hr x 100 cpu ~500K cpu-hrs GCMC Select best ~5K Select best ~5K

43. DOCK on SiCortex CPU cores: 5760 Power: 15,000 W Tasks: 92160 Elapsed time: 12821 sec Compute time: 1.94 CPU years (does not include ~800 sec to stage input data) Ioan Raicu, Zhao Zhang

44. An NSF MRI Proposal: PADS: Petascale Active Data Store 500 TB reliable storage (data & metadata) 180 TB, 180 GB/s 17 Top/s analysis Data ingest Dynamic provisioning Parallel analysis Remote access Offload to remote data centers P A D S Diverse users Diverse data sources 1000 TB tape backup

45. Using Computation to Accelerate Science Complex modeling Experiment automation Data analysis Collaboration & federation Hypothesis generation

46. Integrated View of Simulation, Experiment, & Informatics *Simulation Information Management System + Laboratory Information Management System Database Analysis Tools Experiment SIMS* Problem Specification Simulation Browsing & Visualization LIMS + Experimental Design Browsing & Visualization

47. Robot Scientist (University of Wales)

48. A Concluding Thought “ Applied computer science is now playing the role that mathematics did from the 17th through the 20th centuries: providing an orderly, formal framework & exploratory apparatus for other sciences.” George Djorgovski

49. The Computation Institute A joint institute of Argonne and the University of Chicago, focused on furthering system-level science via the development and use of advanced computational methods. Solutions to many grand challenges facing science and society today require the analysis and understanding of entire systems, not just individual components. They require not reductionist approaches but the synthesis of knowledge from multiple levels of a system, whether biological, physical, or social (or all three). www.ci.uchicago.edu Faculty, fellows, staff, students, computers, projects.