Ken Wilson observed: computational science a third mode of enquiry in addition to experiment and theory. My theme is rather how, by taking a systems view of the knowledge generation process, we can identify ways in which computation can accelerate.
Computation and Knowledge Ian Foster Computation Institute Argonne National Lab & University of Chicago
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.
Discovery (1): Registries Duke OSU NCI NCI Globus
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
(does not include ~800 sec to stage input data) Ioan Raicu, Zhao Zhang
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
Using Computation to Accelerate Science Complex modeling Experiment automation Data analysis Collaboration & federation Hypothesis generation
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
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).