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Cluster-based Visualization. Dino Pavlakos Sandia National Laboratories Albuquerque, New Mexico. High End Graphics Platforms. 10 9. 10 8. SGI Graphics. Polygon Rendering Rate (Megapolys/Second). 10 7. PC Graphics. 10 6. 10 3. 1999. 2001. 2004. 100 Tflops. 3 Tflops. 10 Tflops.

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Cluster based visualization

Cluster-based Visualization

Dino Pavlakos

Sandia National Laboratories

Albuquerque, New Mexico


High end graphics platforms
High End Graphics Platforms

109

108

SGI Graphics

Polygon Rendering Rate

(Megapolys/Second)

107

PC Graphics

106

103

1999

2001

2004

100

Tflops

3

Tflops

10

Tflops

Years/Compute Performance


Rendering and sorting
Rendering and Sorting

Sort Last

Sort Middle

Sort First

Polygon Rendering Pipe


Tiled vs single composite displays

Renderer

Renderer

Renderer

Display(s)

Renderer

Tiled vs. Single / Composite Displays

Tiled

Composite

Renderer

Renderer

Display

Renderer

Renderer


Data exploration architecture

Reducedpolys/data

Data Exploration Architecture

Data

Archive

Big Data

ComputeService

DataService

Vis.Service

UserWork-

station

Polys/Data

Images

Simulate

Compress

RenderDec/C

DI

DecompressUser

dI

render

Dec/C

DD

Dd

DI

Archive


Tightly coupled compute data services and visualization
Tightly Coupled Compute, Data Services and Visualization

InfiniBand x12

Link Speed:

6 GB/s Bidirectional

(3 GB/s each way)

16 rows

16 rows

Aggregate bandwidth across vertical plane:

768 GB/s each way, with 256 (16 x 16) rows

(exceeds I/O requirements)


Visualization data service clusters @ snl
Visualization/Data Service Clusters @ SNL

Existing

  • 16-node SGI/320 NT graphics cluster (GigE)

  • 72 node Compaq/NT data service cluster (ServerNet)

    Coming

  • 64 node graphics cluster (ASCI V1 Corridor)

  • 8-16 node graphics cluster (open testbed)


Cluster based visualization issues
Cluster-based visualization issues

  • Rendering scalability vs. interactive latency

    • Expect good results for rendering large data

    • Getting high frame rates (e.g. 60Hz) harder

  • Dynamic resource management

  • Desktop access to large resource

  • Parallel/Scalable IO

  • Parallel inter-process communication (runtime visualization & data services, computational experiments)

  • Classified/Unclassified use


Cluster based composite rendering simplistic projection
Cluster-based Composite RenderingSimplistic Projection

  • 16 node SGI/320 cluster

  • Peak 4 Million polygons/sec. per node

  • 16 x 4 = 64 Million polygons/sec. peak (perfect scaling)

  • Assume

    • 64 Million-Polygon surface data

    • Sort-last rendering (HW-accelerated)

    • 1K x 1K Display

  • Render 64M polygons in 1 sec.

  • Add .84 sec. composite time (Compaq NT cluster / ServerNet)

    • gives 35M polygons/sec.

  • Add .16 sec. composite time (ASCI Red)

    • gives 55M polygons/sec.


Parallel visualization abstract partitioning model
Parallel Visualization Abstract Partitioning Model

DataRepository/Buffer

DataRepository/Buffer

...

AbstractData

Space

(Parallel Disk,Shared Memory,

Dist. MemoryBuffer, …)

DataInterface

SimulationCode

Data ServiceModule

VisualizationModule

...

ControlInterface


Do it all on c plant instantiation
Do-It-All on C-Plant Instantiation

...

AbstractData

Space

Disk1

Disk2

DiskN

...

Comp 1

...

Comp 2

Node1

Node2

NodeN

Comp N


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