Presentation on theme: "SAN DIEGO SUPERCOMPUTER CENTER at the University of California, San Diego Science, Engineering, Technology… (and the Facilities that Support them) San."— Presentation transcript:
SAN DIEGO SUPERCOMPUTER CENTER at the University of California, San Diego Science, Engineering, Technology… (and the Facilities that Support them) San Diego Supercomputer Center University of California, San Diego Net@EDU Annual Meeting February 5, 2007 Dallas Thornton IT Director, SDSC
SAN DIEGO SUPERCOMPUTER CENTER at the University of California, San Diego SDSC in a nutshell Employs nearly 400 researchers, staff and students UCSD Organized Research Unit Strategic Focus on Data-Oriented Scientific Computing Home of many associated activities including Geosciences Network (GEON) Network for Earthquake Engineering Simulation IT (NEESit) Protein Data Bank (PDB) Joint Center for Structural Genomics Alliance for Cell Signaling (AfCS) Biomedical Informatics Research Network (BIRN) Coordinating Center High Performance Wireless Research and Education Network (HPWREN) Grid and Cluster Computing Integrated Biosciences Networking High-end computing Data and Knowledge Systems Integrated Computational Sciences
SAN DIEGO SUPERCOMPUTER CENTER at the University of California, San Diego A Partial List of Databases and Data Collections currently housed at SDSC Protein Data Bank (protein data) National Virtual Observatory (astronomical data) UCSD Libraries Image Collegion (ArtStore) National Science Digital Library (education collection) SCEC (earthquake data) BIRN (neuroscience data) Encyclopedia of Life (genomic data) Protein Kinase Resource (protein data) TreeBase (phylogeny and ontology information) Transport Classification Database (protein information) PlantsP (plant kinase information) PlantsT (plant transporter information) PlantsUBQ (plant protein information) CKAAPS (protein evolutionary information) AfCS Molecule Pages (protein information) SLACC-JCSG (structural genomics data) APOPTOSIS DB (proteins related to cell death data) NAVDAT (geochemistry data) QRC (NSF data on Supercomputer Centers and PACI) Network Topology Data (Skitter project) Biology Workbench Databases (mirrors and “originals” of over 80 biology databases) San Diego and Tijuana Watersheds (water resources mapping) PETDB (petrological and chemical data) Seamount Catalogue (bathymetric seamount maps) IPBIR (primate information) Hayden Planetarium Collection (astronomical data) TeraGrid Data (science and engineering collections) Digital Embryo (human embryology) National Archives (persistent archive) San Diego Conservation Resources Network (sensitive species map server) Bionome (Biology network of modeling efforts) KNB (Knowledge networks for biocomplexity) LDAS (land data assimilation system) SEEK (ecology data) ROADNET (sensor data) NPACI Data Grid (scientific simulation output) Salk (biology data archive) CUAHSI (community hydrological collection) Backbone Packet Header Traces (OC48, OC12) 2 Micron All Sky Survey (astronomy data) Digital Palomar Observatory Sky Survey Collection (astronomy data) Sloan Digital Sky Survey Collection (astronomy data) Interpro Mirror (protein data) HPWREN Wireless Network Network Analysis Data HPWREN Sensor Network Data Security logs and archives (security information) Nobel Foundation Mirror (information) EarthRef Digital Archive (Earth Science information) GERM (earth reservoir information) PMAG (paleomagnetic information) GEOROC (petrological and geochemical data for igneous rocks) Kd’s DB (rocks and minerals) Braindata (Rutgers neuroscience collection) LTER (hyperspectral images) SIO-Explorer (oceanographic voyages) Scripps (oceanographic research data) Transana (classroom video) WebBase (web crawls) Alexandria Digital Library (photographs) Backskatter Data (from UCSD network telescope) Digital Earth Data Library (earth sciences related datasets)
SAN DIEGO SUPERCOMPUTER CENTER at the University of California, San Diego SDSC’s Funding Federal Grants State Support Campus Support Industry Partnerships Recharge / Fee For Service Leverage Economies of Scale Labor – Consulting, Support, Sys Management, etc. Storage Compute Cycles Collocation/Hosting Services
SAN DIEGO SUPERCOMPUTER CENTER at the University of California, San Diego SDSC’s Evolutionary Datacenter Privately-built 7,000 sq ft. in 1985 Transitioned to UCSD in 1997 Expanded to 11,000 sq. ft. in 2001 Expanded to 14,000 sq. ft. in 2006 Expanding to 19,000 sq. ft. in 2008 Power and Cooling Requirements Grew and Changed with New Systems Previous upgrades have been costly. Developing a scalable power and cooling infrastructure with UCSD facilities to accommodate future systems.
SAN DIEGO SUPERCOMPUTER CENTER at the University of California, San Diego Lessons Learned (or Learning) Maximize yield from the build and upgrades Incremental upgrades are exceedingly expensive! Engineer the facility for 2x-4x power, cooling, and space expansion capability... (No matter what the architects say.) Decide where to invest your money 2N configurations, UPSes, Generators, etc. are great but usually too expensive to be worthwhile for large research clusters. Evaluate systems in need of this reliability and build accordingly. Consider different rates for this extra level of service. Be on the same page with campus facilities Ensure newly-installed distribution paths provide spare capacity. Carefully evaluate utilities costs in site selection. Standardize, standardize, standardize!
SAN DIEGO SUPERCOMPUTER CENTER at the University of California, San Diego Q&A
SAN DIEGO SUPERCOMPUTER CENTER at the University of California, San Diego The Density Problem Note Log Scale 10kW Racks in 2005 will be 100kW in 2010 Rising Density + Reduced Costs = Exponential Demand Growth HPC Even More Dense
SAN DIEGO SUPERCOMPUTER CENTER at the University of California, San Diego Who pays for the facilities? PIs / Faculty What do my indirect costs pay for, anyways? This varies widely by institution, but IDCs do not scale well with the facilities requirements of machines over time. Need to budget incremental facilities costs in grants. Grantors Facilities should be funded by the state. As the costs to operate and maintain increasingly facilities-hungry systems increase, states are less capable of providing adequate support. Need to support incremental facilities costs in grants. Campuses/States The grantor should pay the costs of the grant’s needs. A valid argument, but if the state/campus wants to be competitive with their proposal, some subsidy is required. Need to develop a scalable model to incrementally fund facilities, decide how much this will be subsidized, and get buy-in from PIs and Faculty.