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National Institute for Computational Sciences

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  1. NICS is a collaboration between the University of Tennessee and ORNL Awarded the NSF Track 2B-Kraken (1PF) Remote Data Analysis and Visualization –Nautilus (Sean Ahern) Experimental GPGPU system – Keeneland (Jeff Vetter) National Institute for Computational Sciences

  2. Cray XT5 system – October 2009 8,256 two-socket nodes 16,512 six-core AMD Istanbul processors 99,072 cores (2.6 GHz) 129 TB memory 1,030 teraflops

  3. NICS User Accounts and Projects # Date

  4. HPSS Usage # Date

  5. Kraken Job Mix (March, 2010)

  6. Kraken Utilization (weekly)

  7. Nautilus Versions: all SGI Ultraviolet, running SLES 11 OS • P0 (half-rack) • 128 Cores • 256 GB RAM • 1 GPU • P1 (1 rack) • 256 Cores • 1 TB RAM • 4 GPUs • Final System (4 racks) • 1024 Cores • 4 TB RAM • 16 GPUs

  8. Nautilus Delivery Schedule

  9. Remote Data Analysis & Visualization Events RDAV resources are currently in the allocations system, and several requests have been made. Joint visualization class with TACC at the Petascale Programming Environments and Tools classes in early July. A tutorial on Nautilus usage for visualization, data analysis, and workflow management will be taught at TeraGrid'10

  10. Keeneland – An NSF-Funded Partnership to Enable Large-scale Computational Science on Heterogeneous Architectures NVIDIA’s new Fermi GPU • NSF Track 2D System of Innovative Design • Georgia Tech • University of Tennessee, Knoxville • UT National Institute for Computational Sciences • ORNL • Exploit graphics processors to provide extreme performance and energy efficiency • Deploy two GPU clusters • Initial Delivery – 2010 • Final Delivery – 2012 • NVIDIA, HP, Intel, Qlogic • Software tools, application development • Operations, user support • Education, Outreach, Training for scientists, students, industry • FERMI • capable of over 1 TFs single precision and over 500 GFs double precision • Includes error correction in memory • Includes new level of cache

  11. Keeneland will enable transformational science for those applications currently limited by node level parallelism and memory bandwidth • Node-level extreme fine-grained parallelism and memory bandwidth from GPUs can transform applications that cannot benefit directly from scaling up • Recent applications successes on GPUs: • Molecular modeling (NAMD, VMD, OpenMM, GROMACS, AMBER) • Materials modeling (DCA++, QMCPACK, LAMMPS) • Combustion (S3D) • GPUs are setting a new trajectory for HPC architectures by providing very high energy efficiency and density S3D NAMD DCA++