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Computing Infrastructure for Science

Computing Infrastructure for Science. On-Site Computing. Model for Virtual Data Facility. BER Community Projects. HEP Community Projects. BES Community Projects. X Projects. Virtual Machine Common Access Layer. Virtual Data Facility Common Access Layer. VDF. VDF. VDF.

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Computing Infrastructure for Science

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  1. Computing Infrastructure for Science On-Site Computing

  2. Model for Virtual Data Facility BER Community Projects HEP Community Projects BES Community Projects X Projects . . . Virtual Machine Common Access Layer Virtual Data Facility Common Access Layer VDF VDF VDF Argonne ALCF Berkeley NERSC Oak Ridge OLCF • ESnet

  3. Challenges and Goals of a DOE Data Facility • Availability: seamless cross-site resilient access • Scalability: in data volume • Velocity: real-time steering, processing and storage • Performance: multiple sites • Scalability: in size of user community • Interoperability across software and data sets • Usability: Consistent interfaces for building domain-specific services • Mobility (move data between facilities in & outside the complex) • Efficiency: maximize through economy of scale

  4. Virtual Cross-cutting Facility Upgrade • Leverage existing facilities as base • Creates a seamless facility for storing, analyzing, and sharing data • Built on a common data retention and sharing policy for DOE/SC user facilities Scale Up Exascale Program • Pre-exascale 2016 • Peak Exaflop 2020 • Sustained Exaflop 2024 Scale Out • Support Innovation Hubs/Centers • and Industry • Cluster/Clouds tailored to • domain specific innovation • Common scalable multi-institutional • collaboratoryenvironments • Common scalable remote • visualization and workflow • environments • Infrastructure • Shared file • and archive • Shared • authentication • Support DOE/SC • Experimental Facilities • Data storage • Serve data to • worldwide users • Data analysis & • visualization

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