1. Source: Jim Dolgonas, CENIC CENIC is Removing the Inter-Campus Barriers in California ~ $14M Invested in Upgrade Now Campuses Need to Upgrade

2. The “Golden Spike” UCSD Experimental Optical Core: Ready to Couple Users to CENIC L1, L2, L3 Services Source: Phil Papadopoulos, SDSC/Calit2 (Quartzite MRI PI, OptIPuter co-PI) Funded by NSF MRI Grant Lucent Glimmerglass Force10 OptIPuter Border Router CENIC L1, L2 Services Cisco 6509 Currently: >= 60 endpoints at 10 GigE >= 30 Packet switched >= 30 Switched wavelengths >= 400 Connected endpoints Approximately 0.5 Tbps Arrive at the “Optical” Center of Hybrid Campus Switch

3. Network Today Quartzite

4. Calit2 Sunlight Optical Exchange Contains Quartzite Maxine Brown, EVL, UIC OptIPuter Project Manager

5. What the Network Enables Data, Computing anywhere on Campus Always-on high-resolution streaming Large-scale data movement w/o impacting commodity net. Complete re-factoring of where network- connected resources are located

6. Campus Fiber Network Based on Quartzite Allowed UCSD CI Design Team to Architect Shared Resources UCSD Storage OptiPortal Research Cluster Digital Collections Lifecycle Management PetaScale Data Analysis Facility HPC System Cluster Condo UC Grid Pilot Research Instrument N x 10Gbe DNA Arrays, Mass Spec., Microscopes, Genome Sequencers Source: Phil Papadopoulos, SDSC/Calit2

7. Triton – A Downpayment on Campus-scale CI Standard Compute Cluster (256 nodes, 2048 Cores, 6TB RAM) Large-memory Cluster (28 nodes, 896 cores, 9TB RAM) Large-scale storage –At baby stage with 180TB and 4GB/sec –Goal is ~4PB and 100GB/sec BW Structure managed with Rocks. An open system. Will also function as a high-performance cloud platform

8. TritonResource: Expect initial production on compute systems ~June 2009 Data Oasis storage system expected fall 2009

9. Triton Designed for Particular Apps Overriding need for Large Memory nodes –8 @ 512GB, 20 @ 256GB (4 dedicated as DB’s) A Small Sampling: Regional Ocean Circulation (COMPAS @ Scripps) –Scalable algorithm + single node optimization step (> 150GB memory needed) 3D Tomographic Reconstruction of EM Images (Medicine) –256, 512GB “on the small side” DNA Sequence Analysis with short sequence reads - > 128 GB Human Heart Full Beat Simulation (Bioengineering) –100 – 200 GB Drug discovery and design from first principles.

10. Triton Network Connectivity Total Switch Capacity – 512 X 10 Gbit/sec = 5 Tbit/s ($150K) 32 x 10GbE to Campus Networks including at least 5x10GbE to Quartzite OptIPuter. –All external-to-UCSD high-speed networks could terminate on Triton at full rate Mid Construction – Large Memory Nodes Integrated into Switch (28 nodes, 40Gbit/s/Node)

11. The NSF-Funded GreenLight Project Giving Users Greener Compute and Storage Options Measure and Control Energy Usage: –Sun Has Shown up to 40% Reduction in Energy –Active Management of Disks, CPUs, etc. –Measures Temperature at 5 Levels in 8 Racks –Power Utilization in Each of the 8 Racks –Chilled Water Cooling Systems UCSD Structural Engineering Dept. Conducted Sun MD Tests May 2007 UCSD (Calit2 & SOM) Bought Two Sun MDs May 2008 Source: Tom DeFanti, Calit2; GreenLight PI

12. The GreenLight Project: Instrumenting the Energy Cost of Computational Science Focus on 5 Communities with At-Scale Computing Needs: –Metagenomics –Ocean Observing –Microscopy –Bioinformatics –Digital Media Measure, Monitor, & Web Publish Real-Time Sensor Outputs –Via Service-oriented Architectures –Allow Researchers Anywhere To Study Computing Energy Cost –Enable Scientists To Explore Tactics For Maximizing Work/Watt Develop Middleware that Automates Optimal Choice of Compute/RAM Power Strategies for Desired Greenness Partnering With Minority-Serving Institutions Cyberinfrastructure Empowerment Coalition Source: Tom DeFanti, Calit2; GreenLight PI

13. Research Needed on How to Deploy a Green CI Computer Architecture –Rajesh Gupta/CSE Software Architecture –Amin Vahdat, Ingolf Kruger/CSE CineGrid Exchange –Tom DeFanti/Calit2 Visualization –Falko Kuster/Structural Engineering Power and Thermal Management –Tajana Rosing/CSE Analyzing Power Consumption Data –Jim Hollan/Cog Sci Direct DC Datacenters –Tom Defanti, Greg Hidley http://greenlight.calit2.net MRI

14. New Techniques for Dynamic Power and Thermal Management to Reduce Energy Requirements Dynamic Thermal Management (DTM) Workload Scheduling: Machine learning for Dynamic Adaptation to get Best Temporal and Spatial Profiles with Closed-Loop Sensing Proactive Thermal Management Reduces Thermal Hot Spots by Average 60% with No Performance Overhead Dynamic Power Management (DPM) Optimal DPM for a Class of Workloads Machine Learning to Adapt Select Among Specialized Policies Use Sensors and Performance Counters to Monitor Multitasking/Within Task Adaptation of Voltage and Frequency Measured Energy Savings of Up to 70% per Device NSF Project Greenlight Green Cyberinfrastructure in Energy-Efficient Modular Facilities Closed-Loop Power &Thermal Management System Energy Efficiency Lab (seelab.ucsd.edu) Prof. Tajana Šimunić Rosing, CSE, UCSD