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Dv: A toolkit for building remote interactive visualization services

Dv: A toolkit for building remote interactive visualization services. David O’Hallaron School of Computer Science Carnegie Mellon University Martin Aeschlimann, Peter Dinda, Loukas Kallivokas, Julio Lopez, Bruce Lowekamp. Teora, Italy 1980. Jacobo Bielak and Omar Ghattas (CMU CE)

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Dv: A toolkit for building remote interactive visualization services

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  1. Dv: A toolkit for buildingremote interactive visualization services David O’Hallaron School of Computer Science Carnegie Mellon University Martin Aeschlimann, Peter Dinda, Loukas Kallivokas, Julio Lopez, Bruce Lowekamp

  2. Teora, Italy 1980

  3. Jacobo Bielak and Omar Ghattas (CMU CE) Thomas Gross (CMU CS and ETH Zurich) David O’Hallaron (CMU CS and ECE)

  4. Visualization of 1994 Northridge aftershock

  5. Visualization of 1994 Northridge aftershock

  6. Internet service models request • Traditional lightweight service model • small to moderate amount of computation to satisfy requests • e.g. serving web pages, stock quotes, online trading, search engines • Proposed heayweight service model • massive amounts of computations to satisfy requests • scientific visualization, data mining, medical imaging server client response

  7. Typical Quake visualizationpipeline local display and input FEM solver engine materials database resolution contours ROI scene remote database interpolation isosurface extraction scene synthesis reading rendering vtk library routines

  8. WAN Local compute hosts (allocated once per request by the service user) Remote compute hosts (allocated once per service by the service provider) Heavyweight grid service model

  9. Active frames Active Frame Server Input Active Frame Output Active Frame Active frame interpreter Frame data Frame program Frame data Frame program Application libraries e.g, vtk Host

  10. Overview of a Dv visualization service User inputs Local Dv client Request frame Display Response frames Dv Server (request server) Dv Server Dv Server Dv Server Remote dataset ... Response frames Response frames Response frames Remote DV Active Frame Servers Local DV Active Frame Servers

  11. Grid-enabling vtk with Dv request frame [request server, scheduler, flowgraph, data reader ] local Dv client status result request server local Dv server ... ... reader scheduler response frames (to other Dv servers) [native data, schedule, flowgraph,control ] remote machine (Dv request server) local machine (Dv client)

  12. Scheduling Dv programs • Scheduling at request frame creation time • all response frames use same schedule • performance portability (i.e. adjusting to heterogeneous resources) is possible. • no adaptivity (i.e., adjusting to dynamic resources) • Scheduling at response frame creation time • performance portability and limited adaptivity. • Scheduling at response frame delivery time • performance portability and greatest degree of adaptivity. • per-frame scheduling overhead a potential disadvantage.

  13. Scheduling scenarios Ultrahigh Bandwidth Link powerful local server low-end remote server

  14. Scheduling scenarios High Bandwidth Link high-end remote server powerful local workstation

  15. Scheduling scenarios Low Bandwidth Link high-end remote server local PC

  16. Scheduling scenarios Low Bw Link High Bandwidth Link high-end remote server low-end local PC or PDA powerful local proxy server

  17. Summary • Heavyweight grid service model • service providers can constrain resources allocated to a particular service • service users can contribute resources to improve response time of throughput • Active frames • general software framework for providing heavyweight Internet services • framework can be specialized for a particular service type • Dv • specialized version of active frame server for vizualization • grid-enables existing vtk toolkit • flexible framework for experimenting with scheduling algs

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