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Project StarGate An End-to-End 10Gbps HPC to User Cyberinfrastructure  ANL  * Calit2 * LBNL * NICS * ORNL *  SDSC Report to the  Dept. of Energy Advanced Scientific Computing Advisory Committee Oak Ridge, TN November 3, 2009 Dr. Larry Smarr Director, California Institute for Telecommunications and Information Technology Harry E. Gruber Professor,  Dept. of Computer Science and Engineering Jacobs School of Engineering, UCSD Twitter: lsmarr
Project StarGate ANL  *  Calit2  *  LBNL  *  NICS  *  ORNL  *   SDSC
Credits ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Exploring Cosmology With Supercomputers, Supernetworks, and Supervisualization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Science:  Norman, Harkness,Paschos SDSC Visualization:  Insley, ANL; Wagner SDSC ,[object Object],Intergalactic medium on 2 Glyr scale
Project StarGate Goals ,[object Object],[object Object],[object Object],[object Object],[email_address] Rick Wagner Mike Norman ,[object Object]
Why Supercomputer Centers Shouldn’t Be  Data Black Holes or Island Universes ,[object Object],[object Object],[object Object],[object Object]
Opening Up 10Gbps Data Path ORNL/NICS to ANL to SDSC Connectivity provided by ESnet Science Data Network End-to-End Coupling of User with DOE/NSF HPC Facilities
StarGate Network & Hardware ALCF DOE Eureka 100 Dual Quad Core  Xeon Servers 200 NVIDIA Quadro FX GPUs in 50 Quadro Plex S4 1U enclosures 3.2 TB RAM SDSC NICS Calit2/SDSC OptIPortal1 20 30” (2560 x 1600 pixel) LCD panels 10 NVIDIA Quadro FX 4600 graphics cards > 80 gigapixels 10 Gb/s network throughout NSF TeraGrid Kraken Cray XT5 8,256 Compute Nodes 99,072 Compute Cores 129 TB RAM simulation rendering visualization Science Data Network (SDN) > 10 Gb/s fiber optic network Dynamic VLANs configured using OSCARS ESnet ,[object Object],Challenge: Kraken  is not on ESnet
StarGate Streaming Rendering ALCF SDSC flPy, a parallel (MPI) tiled image/movie viewer composites the individual movies, and synchronizes the movie playback across the OptIPortal rendering nodes. ESnet Simulation volume is rendered using vl3 , a parallel (MPI) volume renderer utilizing Eureka’s GPUs. The rendering changes views steadily to highlight 3D structure. A media bridge at the border provides secure access to the parallel rendering streams. gs1.intrepid.alcf.anl.gov ALCF Internal 1 The full image is broken into subsets (tiles). The tiles are continuously encoded as a separate movies. 2 3 4 Updated instructions are sent back to the renderer to change views, or load a different dataset. 5 ,[object Object]
Test animation of  1/64  of the data volume (1024 3   region) www.mcs.anl.gov/~insley/ENZO/BAO/B4096/enzo-b4096-1024subregion-test.mov ,[object Object]
Data Moved ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ANL Eureka Graphics Cluster ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Visualization Pipeline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
vl3 Rendering Performance on Eureka ,[object Object],[object Object],Note Data I/O Bottleneck ,[object Object],Data Size Number of Processors/ Graphics Cards Load Time Render/Composite Time 2048 3 17 2min 27sec 9.22 sec 4096 3 129 5min 10sec 4.51 sec 6400 3  (AMR) 129 4min 17sec 13.42sec
Next Experiments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Panel: Open Infrastructure for an Open Society: OSG, Commercial Clouds, and B...Panel: Open Infrastructure for an Open Society: OSG, Commercial Clouds, and B...
Panel: Open Infrastructure for an Open Society: OSG, Commercial Clouds, and B...
 
Panel: Open Infrastructure for an Open Society: OSG, Commercial Clouds, and B...
Panel: Open Infrastructure for an Open Society: OSG, Commercial Clouds, and B...Panel: Open Infrastructure for an Open Society: OSG, Commercial Clouds, and B...
Panel: Open Infrastructure for an Open Society: OSG, Commercial Clouds, and B...
 
Frank Würthwein - NRP and the Path forward
Frank Würthwein - NRP and the Path forwardFrank Würthwein - NRP and the Path forward
Frank Würthwein - NRP and the Path forward
 

Project StarGate An End-to-End 10Gbps HPC to User Cyberinfrastructure ANL * Calit2 * LBNL * NICS * ORNL * SDSC

  • 1. Project StarGate An End-to-End 10Gbps HPC to User Cyberinfrastructure ANL * Calit2 * LBNL * NICS * ORNL * SDSC Report to the Dept. of Energy Advanced Scientific Computing Advisory Committee Oak Ridge, TN November 3, 2009 Dr. Larry Smarr Director, California Institute for Telecommunications and Information Technology Harry E. Gruber Professor, Dept. of Computer Science and Engineering Jacobs School of Engineering, UCSD Twitter: lsmarr
  • 2. Project StarGate ANL * Calit2 * LBNL * NICS * ORNL * SDSC
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  • 7. Opening Up 10Gbps Data Path ORNL/NICS to ANL to SDSC Connectivity provided by ESnet Science Data Network End-to-End Coupling of User with DOE/NSF HPC Facilities
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Editor's Notes

  1. NSF TeraGrid Review January 10, 2006 Charlie Catlett (cec@uchicago.edu) Eureka – the visualization cluster at ALCF Each node has 2 graphics cards 8 processors 32 GB RAM fast interconnect local disk Server FLOPS = 2.0 GHz * 8 cores * 2 flop per clock = 32 GFLOPS
  2. NSF TeraGrid Review January 10, 2006 Charlie Catlett (cec@uchicago.edu) One of its strengths is its speed, and ability to handle large data sets. Number of procs = power of 2 to do rendering + 1 for compositing 2 graphics cards per node, so half as many nodes as listed here Data i/o is clearly the bottleneck Doing an animation of a single time step, data is only loaded once, can be pretty quick