paraview workshop: distributed visualization samar aseeri, ksl madhu srinivasan, kvl
TRANSCRIPT
![Page 1: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/1.jpg)
Paraview Workshop:Distributed Visualization
Samar Aseeri, KSL
Madhu Srinivasan, KVL
![Page 2: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/2.jpg)
Visualization Allows Us to “See” the Science
Application Render
Geometric Primitives PixelsRaw Data
01001101011001
11001010010101
00101010100110
11101101011011
00110010111010
![Page 3: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/3.jpg)
But what about large, distributed data?01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
![Page 4: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/4.jpg)
Or distributed rendering?
01001101011001
11001010010101
00101010100110
11101101011011
00110010111010
![Page 5: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/5.jpg)
Or distributed displays?
01001101011001
11001010010101
00101010100110
11101101011011
00110010111010
![Page 6: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/6.jpg)
Or all three?01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
![Page 7: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/7.jpg)
Visualization Scaling Challenges
• Moving data to the visualization machine
• Most applications built for shared memory machines, not distributed clusters
• Image resolution limits in some software cannot capture feature details
• Displays cannot show entire high-resolution images at their native resolution
![Page 8: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/8.jpg)
Visualization scales with HPC• Large data produced by large
simulations require large visualization machines and produce large visualization results
Terabytes of Data
AT LEASTTerabytes
of Vis
Gigapixel Images
Resampling, Application,
…
Resolution to Capture
Feature Detail
![Page 9: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/9.jpg)
Moving Data
• How long can you wait?
File Size 10 Gbps 54 Mbps
1 GB 1 sec 2.5 min
1 TB ~17 min ~43 hours
1 PB ~12 days ~5 years
![Page 10: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/10.jpg)
Analyzing Data• Visualization programs only beginning to
efficiently handle ultrascale data– 650 GB dataset -> 3 TB memory footprint– Allocate HPC nodes for RAM not cores– N-1 idle processors per node!
• Stability across many distributed nodes– Rendering clusters typically number N <= 64– Data must be dividable onto N cores
Remember this when resampling!
![Page 11: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/11.jpg)
ParaView Architecture•Three tier
–Data Server–Render Server–Client
![Page 12: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/12.jpg)
Standalone
Client
DataServer
RenderServer
![Page 13: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/13.jpg)
Client-Server
ClientData
ServerRenderServer
![Page 14: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/14.jpg)
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
Raw Data Geometry
Pixels
Client-Server
ClientData
ServerRenderServer
![Page 15: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/15.jpg)
Client-Render Server-Data Server
ClientData
ServerRenderServer
![Page 16: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/16.jpg)
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
01001101011001 01001101011001
11001010010101 11001010010101
00101010100110 00101010100110
11101101011011 11101101011011
00110010111010 00110010111010
Raw Data Geometry
Pixels
Client-Render Server-Data Server
ClientData
ServerRenderServer
![Page 17: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/17.jpg)
Requirements forInstalling ParaView Server
•C++•CMake (www.cmake.org)•MPI•OpenGL (or Mesa3D www.mesa3d.org)•Qt 4.6 (optional)•Python (optional)• http://www.paraview.org/Wiki/Setting_up_a_ParaView_Server#Compiling
![Page 18: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/18.jpg)
Connecting to a ParaView Server
http://www.paraview.org/Wiki/Setting_up_a_ParaView_Server#Running_the_Server
DEMO
![Page 19: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/19.jpg)
Data Parallel Pipelines•Duplicate pipelines run independently on different partitions of data.
![Page 20: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/20.jpg)
Data Parallel Pipelines•Duplicate pipelines run independently on different partitions of data.
![Page 21: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/21.jpg)
Data Parallel Pipelines•Some operations will work regardless.
–Example: Clipping.
![Page 22: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/22.jpg)
Data Parallel Pipelines•Some operations will work regardless.
–Example: Clipping.
![Page 23: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/23.jpg)
Data Parallel Pipelines•Some operations will work regardless.
–Example: Clipping.
![Page 24: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/24.jpg)
Data Parallel Pipelines•Some operations will have problems.
–Example: External Faces
![Page 25: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/25.jpg)
Data Parallel Pipelines•Some operations will have problems.
–Example: External Faces
![Page 26: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/26.jpg)
Data Parallel Pipelines•Ghost cells can solve most of these problems.
![Page 27: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/27.jpg)
Data Parallel Pipelines•Ghost cells can solve most of these problems.
![Page 28: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/28.jpg)
Data Partitioning•Partitions should be load balanced and spatially coherent.
![Page 29: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/29.jpg)
Data Partitioning•Partitions should be load balanced and spatially coherent.
![Page 30: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/30.jpg)
Data Partitioning•Partitions should be load balanced and spatially coherent.
![Page 31: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/31.jpg)
Load Balancing/Ghost Cells•Automatic for Structured Meshes.•Partitioning/ghost cells for unstructured is “manual.”
•Use the D3 filter for unstructured– (Filters → Alphabetical → D3)
![Page 32: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/32.jpg)
Job Size Rules of Thumb•Structured Data
–Try for max 20 M cell/processor.–Shoot for 5 – 10 M cell/processor.
•Unstructured Data–Try for max 1 M cell/processor.–Shoot for 250 – 500 K cell/processor.
![Page 33: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/33.jpg)
Avoiding Data Explosion•Pipeline may cause data to be copied, created, converted.
•This advice only for dealing with very large amounts of data.–Remaining available memory is low.
![Page 34: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/34.jpg)
Topology Changing, No Reduction• Append Datasets• Append Geometry• Clean• Clean to Grid• Connectivity• D3• Delaunay 2D/3D• Extract Edges• Linear Extrusion• Loop Subdivision
• Reflect• Rotational Extrusion• Shrink• Smooth• Subdivide• Tessellate• Tetrahedralize• Triangle Strips• Triangulate
![Page 35: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/35.jpg)
Topology Changing,Moderate Reduction
•Clip •Decimate•Extract Cells by Region
•Extract Selection•Quadric Clustering•Threshold
Similar: Extract Subset
![Page 36: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/36.jpg)
Topology Changing,Dimension Reduction
•Cell Centers•Contour •Extract CTH Fragments•Extract CTH Parts•Extract Surface
•Feature Edges•Mask Points•Outline (curvilinear)•Slice •Stream Tracer
![Page 37: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/37.jpg)
Adds Field Data• Block Scalars• Calculator • Cell Data to Point Data• Compute Derivatives• Curvature• Elevation• Generate Ids• Gen. Surface Normals• Gradient• Level Scalars• Median• Mesh Quality
• Octree Depth Limit• Octree Depth Scalars• Point Data to Cell Data• Process Id Scalars• Random Vectors• Resample with dataset• Surface Flow• Surface Vectors• Texture Map to…• Transform• Warp (scalar)• Warp (vector)
![Page 38: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/38.jpg)
Total Shallow Copy orOutput Independent of Input
• Annotate Time• Append Attributes• Extract Block• Extract Datasets• Extract Level • Glyph • Group Datasets • Histogram• Integrate Variables• Normal Glyphs• Outline
• Outline Corners• Plot Global Variables Over Time• Plot Over Line• Plot Selection Over Time• Probe Location• Temporal Shift Scale• Temporal Snap-to-Time-Steps• Temporal Statistics
![Page 39: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/39.jpg)
Special Cases•Temporal Filters
–Temporal Interpolator–Particle Tracer–Temporal Cache
•Programmable Filter
![Page 40: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/40.jpg)
Tips: Culling Data•Reduce dimensionality early.
–Contour and slice “see” inside volumes.•Prefer data reduction over extraction.
–Slice instead of Clip.–Contour instead of Threshold.
•Only extract when reducing an order of magnitude or more.–Can still run into troubles.
![Page 41: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/41.jpg)
Tips: Culling Data•Experiment with subsampled data.
–Extract Subset•Use caution.
–Subsampled data may be lacking.–Use full data to draw final conclusions.
![Page 42: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/42.jpg)
Memory Inspector
![Page 43: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/43.jpg)
Rendering Modes•Still Render
–Full detail render.•Interactive Render
–Sacrifices detail for speed.–Provides quick rendering rate.–Used when interacting with 3D view.
![Page 44: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/44.jpg)
Level of Detail (LOD)•Geometric decimation•Used only with Interactive Render
Original Data Divisions: 50x50x50 Divisions: 10x10x10
![Page 45: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/45.jpg)
3D Rendering ParametersEdit → Settings, Render View → General
![Page 46: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/46.jpg)
Parallel Rendering
![Page 47: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/47.jpg)
Parallel Rendering
![Page 48: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/48.jpg)
Tiled Displays
![Page 49: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/49.jpg)
Image Size LOD•ParaView’s parallel rendering overhead proportional to image size.
•Can use smaller images for interactive rendering.
Original Data Subsample Rate: 2 pixels Subsample Rate: 4 pixels Subsample Rate: 8 pixels
![Page 50: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/50.jpg)
Parallel Rendering ParametersEdit → Settings, Render View → Server
![Page 51: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/51.jpg)
Parameters for Large Data•Use Immediate Mode Rendering off for GPU, on for CPU.
•Try LOD Threshold off.–Also try LOD Resolution 10x10x10.
•Always have remote rendering on.•Turn on subsampling.•Image Compression on.
![Page 52: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/52.jpg)
Parameters for Low Bandwidth•Try larger subsampling rates.•Try Zlib compression and fewer bits.
![Page 53: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/53.jpg)
Parameters for High Latency•Turn up Remote Render Threshold.•Turn on Use outline for LOD rendering if necessary.
•Play with the LOD Threshold and LOD Resolution to control geometry sent to client.
•Try turning on Lock Interactive Render.
![Page 54: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/54.jpg)
Summary• Challenges at every stage
of visualization when operating on large data
• Partial solutions exist, though not integrated
• Problem sizes continue to grow at every stage
• Vis software community must keep pace with hardware innovations
![Page 55: Paraview Workshop: Distributed Visualization Samar Aseeri, KSL Madhu Srinivasan, KVL](https://reader036.vdocument.in/reader036/viewer/2022062407/56649e445503460f94b3821d/html5/thumbnails/55.jpg)
Thank you !