1. 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

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

6. Internet service models 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 clientserver request response

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

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

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

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

11. Grid-enabling vtk with Dv reader local Dv client response frames (to other Dv servers) [native data, schedule, flowgraph,control ] request frame [request server, scheduler, flowgraph, data reader ] request server remote machine (Dv request server) status... local Dv server scheduler result... 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 low-end remote server powerful local 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 High Bandwidth Link high-end remote server low-end local PC or PDA Low Bw Link 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