final round table on petascale to yotta scale computing and turbulence bengaluru, december 16, 2011

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3 Algorithms, efficiency of codes, and visualization of structures Big runS: Large Reynolds (non-local interactions) vs. long time (memory effects), precision vs. noise, ensemble averaging, modeling vs. vs.... ^ Hydrogen atom vs. complex molecules: 2+ inertial ranges, physics, thermodynamics, chemistry, radiation, boundaries, geometry, … ^ Data: storing, transferring, visualizing, analyzing, sharing (format) ^ Specialized vs. versatile codes, specialized vs. versatile CPUs ^ Size of teams, collaborations across disciplines, across borders, …

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Final round table on petascale to yotta scale computing and Turbulence Bengaluru, December 16, 2011 2 Algorithms, efficiency of codes, and visualization of structures 3 Algorithms, efficiency of codes, and visualization of structures Big runS: Large Reynolds (non-local interactions) vs. long time (memory effects), precision vs. noise, ensemble averaging, modeling vs. vs.... ^ Hydrogen atom vs. complex molecules: 2+ inertial ranges, physics, thermodynamics, chemistry, radiation, boundaries, geometry, ^ Data: storing, transferring, visualizing, analyzing, sharing (format) ^ Specialized vs. versatile codes, specialized vs. versatile CPUs ^ Size of teams, collaborations across disciplines, across borders, 4 Algorithms, efficiency of codes, and visualization of structures Big runS: Large Reynolds (non-local interactions) vs. long time (memory effects), precision vs. noise, ensemble averaging, modeling vs. vs.... ^ Hydrogen atom vs. complex molecules: 2+ inertial ranges, physics, thermodynamics, chemistry, radiation, boundaries, geometry, ^ Data: storing, transferring, visualizing, analyzing, sharing (format) ^ Specialized vs. versatile codes, specialized vs. versatile CPUs ^ Size of teams, collaborations across disciplines, across borders, Seamless prediction across scales: 3-mode models, shells, closures, low dim., filtering, LES, DNS, multi-grid, AMR and combining them 5 Algorithms, efficiency of codes, and visualization of structures Big runS: Large Reynolds (non-local interactions) vs. long time (memory effects), precision vs. noise, ensemble averaging, modeling vs. vs.... ^ Hydrogen atom vs. complex molecules: 2+ inertial ranges, physics, thermodynamics, chemistry, radiation, boundaries, geometry, ^ Data: storing, transferring, visualizing, analyzing, sharing (format) ^ Specialized vs. versatile codes, specialized vs. versatile CPUs ^ Size of teams, collaborations across disciplines, across borders, Seamless prediction across scales: 3-mode models, shells, closures, low dim., filtering, LES, DNS, multi-grid, AMR and combining them Applications: large-Reynolds barrier vs. multi dimensionless parameters Two-dimensional: Euler, NS (log correction?), beta plane, MHD, Rotation, stratification, moisture, boundary layer, anisotropy Astrophysics, magnetic fields and plasmas 6 A big thank-you to the organizers! Whats next?