1. Institute for Mathematical Modeling RAS 1 Visualization in distributed systems. Overview. Remote visualization means interactive viewing of three dimensional scientific data sets over the global network. Scientists use remote parallel computer recourses in many scientific simulations. Scientific data sets are in the gigabyte or even terabyte size range. It is impossible or unreasonable to send the entire data set over the network. Moreover, the client usually has a limited amount of memory and CPU power for viewing and analyzing the data, and scientific data are too large to be processed by a single computer. So, we need powerful visualization tools to analyze massive data sets. Parallel visualization is a solution. M.Iakobovski. P.Krinov, S.Muraviov

2. Institute for Mathematical Modeling RAS 2 Visualization in distributed systems. Goals. R emote V iewer is aimed to quickly and easily process and visualize massive data sets (3D CFD simulations results). Scalar data such as pressure or temperature may be viewed as a series of iso- surfaces and/or as a series of slices Vector data such as the velocity field can be interactively explored using trajectories MeshFlow over plane Iso-surfaces M.Iakobovski. P.Krinov, S.Muraviov

3. Institute for Mathematical Modeling RAS 3 Visualization in distributed systems. Problem statement. RemoteViewer Scalar fields visualization Vector fields visualization Data compression Parallel and grid realization StructureVisualization of Scalar and Vector fields Iso-surfaces for scalar fields Trajectories for vector field Cubic & tetrahedral meshes R emote V iewer Tecplot Computational Server Data Server Visualization Server  Client Workspace M.Iakobovski. P.Krinov, S.Muraviov

4. Institute for Mathematical Modeling RAS 4 Visualization in distributed systems. Issues. a) Edge removal b) Node removal c) Topology refinement a) b) c) Synthesis Reduction Vector data visualization General visualization technique – geometric visualization. Main approaches Experimental analog: to the flow visualization: Path line calculations(individual trajectory) Streakline calculation (fog or smoke) Timelines calculations (coloration) Basic principle - computation of massless particle trajectories Data animation Scalar data visualization methods: Syntesis & Reduction M.Iakobovski. P.Krinov, S.Muraviov

5. Institute for Mathematical Modeling RAS 5 Visualization in distributed systems. Status. Operations on visualization server Data processing Iso-surfaces compression Data transferring to the client Operations on Client Setting of boundaries of the visual area (zoom) and resolution Setting of basic image characteristics (number of iso-surfaces, represented on the screen, corresponding function values; number of trajectories of particles and coordinates of their starting points) The 3D image is displayed on the client computer screen and can be explored using rotation and zooming without referring back to the server If the closer examination of a smaller object fragment is required, the demand for image of this fragment is sent to the server The new image can approximate the object with the higher accuracy due to the reduction in data size M.Iakobovski. P.Krinov, S.Muraviov