Byungil Jeong

Electronic Visualization Laboratory, University of Illinois at Chicago


Visualcasting Scalable Real-Time Image Distribution

in Ultra-High Resolution Display Environments

Byungil Jeong


Introduction

• Data-intensive domains rely on Grid technology and visualization.• The need for a infrastructure to support collaborative work has grown

dramatically.

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Scalable Adaptive Graphics Environment (SAGE)

• SAGE is a specialized middleware for real-time streaming of extremely high-resolution graphics and high-definition video.

• The streams come from remote clusters to scalable display walls over ultra high-speed networks (tens of gigabit).

• Multiple applications (Multitasking)

• Desktop managing: Window move, resize and overlap

• Scalable to LambdaVision: an 11x5 tiled display, 100 Mega-pixel resolution

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Proposed Solution

• A fundamental requirement of high-resolution collaborative visualization systems is multicast of visualization.

• Visualcasting: scalable real-time image multicasting service in ultra-high resolution display environment.

• SAGE Bridge: A high-speed bridging system which distributes pixel data received from rendering clusters to each end-point.

• It is deployed on a high-performance PC cluster equipped with 10gigabit network interfaces.

• It incrementally allocates bridge nodes as the number of endpoints increases.

• It considers heterogeneity of endpoints: different display resolution, computing power and network bandwidth.

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Current and Proposed Models

Current model

Latest SAGE prototype

Proposed model

Visualcasting

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Visualcasting Pipeline

Sending Side (Overloaded)

Rendering Duplication Partition Display

Endpoints

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Introducing SAGE Bridge

Sending Side

Rendering Duplication Partition Display

EndpointsSAGE Bridge

4Mpix1Gbps

10Mpix10Gbps

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Major Contribution and Research Questions

• Extending SAGE to support a scalable real-time image multicasting for tiled displays.

• Enabling distant collaboration with multiple end-points.

• How to arbitrarily scale simultaneous data distribution to multiple receivers?– What parameters define ‘arbitrarily’? – How do those parameters affect the distribution

performance?– If multiple approaches are possible, how to decide the

most appropriate approach based on the parameters?

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Prior Accomplishments

• Designed and implemented a prototype of SAGE– Current Architecture– Dynamic pixel stream reconfiguration– Pixel block based streaming– Achieved Results

• PublicationsJeong, B., Renambot, L et al, “High-Performance Dynamic Graphics Streaming for Scalable Adaptive Graphics Environment,” accepted by Supercomputing 2006.Leigh, J., Renambot, L., Johnson, A., Jeong, B. et al, “The Global Lambda Visualization Facility: An International Ultra-High-Definition Wide-Area Visualization Collaboratory,” Journal of Future Generation Computer Systems, Volume 22, Issue 8, October 2006, pp. 964-971.Renambot, L., Jeong, B. et al, “Collaborative Visualization using High-Resolution Tiled Displays,” CHI 06 Workshop on Information Visualization and Interaction Techniques for Collaboration Across Multiple Displays, April 2006.Jeong, B., Jagodic, R. et al, “Scalable Graphics Architecture for High-Resolution Displays,” IEEE InfoVis Workshop on Using Large, High-Resolution Displays for Information Visualization, October 2005.Renambot, L., Rao, A., Singh, R., Jeong, B. et al, “SAGE: the Scalable Adaptive Graphics Environment,” WACE 2004, September 2004.

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Current SAGE Architecture

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UIclient

App1 App2 App3

Tiled Display

FreeSpaceManager

SAIL SAIL SAIL

SAGE Receiver

SAGE Receiver

SAGE Receiver

SAGE Receiver

UIclient

Pixel Stream

SAGE Messages Synchronization Channel

SAIL: SAGE Application Interface Library


Dynamic Pixel Stream Reconfiguration

• Initial Phase: network connection

• Configuration Phase: configure streams

• Streaming Phase: streaming pixels

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Pixel Block Based Streaming

• SAGE Bridge needs pixel block based streaming.

• Pixel block partition: independent of window layouts

• Incorporated control information (new window layout)

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StreamerSAGE Display

Pixel Block Streaming

StreamerSAGE Display

Image Frame Streaming


Achieved Results

• Scientific visualization at multi-ten Mega-pixel resolutions with interactive frame rates.

• 12 rendering nodes, 1GigE, LAN, UDP: 11.2Gbps, no packet loss.

• 10 rendering nodes, 10Gbps WAN (CaveWAVE), UDP, real application: 9.0Gbps, at most 1% packet loss

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• Pixel block based streaming: a basis of Visualcasting.

• Successful International demonstration at iGrid2005 and SC05

• Used by international collaborators

Network paths and bandwidth used

during iGrid demonstration


Prior Related Works

• Scalable Graphics Engine (SGE, IBM)– A hardware frame buffer for parallel computers– Sixteen 1GigE inputs, 4 Dual-link DVI outputs, 16 Megapixels

• WireGL(Humphreys): sort-first parallel rendering for tiled display

• Chromium(Humphreys), Aura(Germans): distributing visualizations to and from cluster driven tiled-displays

• Distributed Multi-head X11 (XDMX, Martin): X server for a tiled display, supporting chromium, serial applications

• TeraVision(Singh/EVL): scalable, platform-independent, high-resolution video streaming over WAN

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Comparison with Other Approaches

SAGE SGE XDMX Chromium WireGL TeraVision

Multi-tasking (multiple windows) Y Y Y - - -

Window reposition and resizing Y Y Y - - -

Display-rendering decoupling Y Y - - - Y

High-performance WAN support Y - - - - Y

Scalable parallel application support Y Y - Y - -

Scalable image multicasting Y - - - - -

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Proposed SAGE Architecture

• Multiple Free Space Managers

• Each node in the SAGE Bridge cluster is assigned to a sub-image (a group of pixel blocks)

• FSManagers control SAGE Bridge

• SAGE Bridge controls SAIL

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Application Launch Procedures

• A FSManager no longer launches an application.• A SAGE UI has the information about all the FSManagers.• For the second endpoint, the first FSManager directs the

SAGE Bridge to connect to the second FSManager.

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How to Arbitrarily Scale Simultaneous Data Distribution to Multiple Receivers?

• Incremental bridge node allocation: If initially allocated nodes become overloaded, SAGE Bridge allocates additional nodes for the visualcasting session.

• SAIL needs to re-partition images considering newly added nodes and load-balancing.

• New network connections, reconfiguration of existing streams

• What are the conditions for adding or removing SAGE Bridge nodes?

• How to minimize jitter on existing streams?• No additional node available: request SAIL to down-sample

or compress pixel blocks

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How to Decide the Most Appropriate Approach Based on the Parameters?

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Window operation latency

Indirect (B,C) < Partial Indirect (D) < Direct (A)

Network streaming latency

Direct (A) < Indirect (B,C,D)

Load BalancingWhole Indirect (C) > Partial Indirect (D) > Direct (A) > Local Bridge (B)

Re-routed TrafficDirect (A) < Partial Indirect (D) < Whole Indirect (C) < Local Bridge (B)


Partial Indirect Distribution

• Intending to be an optimal solution: a combination of the advantages of other approaches– Less re-routed traffic (Direct distribution - A)– Less hindering display process (Local Bridge - B)– Load Balancing (Whole indirect distribution - C)– Local reconfiguration (B, C)

• Bridge node decision strategy(1) Include the nodes now displaying the application(2) Exclude the nodes heavily used by other applications(3) Preferably include the nodes adjacent to the nodes

selected by (1)(4) Preferably avoid the change of the node set

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What Parameters Define ‘Arbitrarily’?

• Heterogeneous display resolution and bandwidth– Adapt rendering resolution to the smallest tiled display. – SAGE Bridge down-samples pixel blocks for low

resolution displays.– SAGE Bridge compresses pixel blocks for endpoints with

low network bandwidth and high display resolution.

• Heterogeneous computing power– Global data transfer rate may drop down to the data

consume rate of the slowest endpoint.– Down-sample pixel blocks or drop frames for slow

endpoints.

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How Do Those Parameters Affect Performance? - Pixel Block Size -

• Small pixel blocks– Increase flexibility in image partitioning– Increase network API function calls at sending side– Increase OpenGL API function calls at display side

• Large pixel blocks– Increase network overhead due to indivisible pixel block

assumption

• Solutions– Find an optimal pixel block size– Aggregating pixel blocks before network transfer and

downloading to a graphics card– Exceptions to indivisible pixel block assumption

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How Do Those Parameters Affect Performance? - Network Protocol Interfaces -

• Pixel blocks generated from application images are streamed using blocking network send.

• Blocking send slows down wide area reliable network streaming.

• Non-blocking send using LambdaStream can improve performance.

• Requiring an interface to check if pixel block buffers are completely transferred.

• Another interface to request necessary bandwidth and return available bandwidth

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How Do Those Parameters Affect Performance? - Pixel Stream Compression -

• Typical bandwidth utilization of SAGE applications in EVL– Serial application: 60~70% (1GigE)– Parallel application: 20~90% (10Gbps WAN)

• Pixel block compression is a good solution.• SAGE Bridge can distribute compressed pixel blocks

without decompressing them.– Increases Load on SAIL and SAGE Display– Decreases Load on SAGE Bridge– Increases scalability of SAGE Bridge

• Candidate compression techniques: Run Length Encoding, RGB to YUV color transform, DXT compressed texture, Wavelet transform

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Comparison with Multicasting Approaches

• IP multicast and reliable layered multicast: applicable to multicast-enabled networks

• No intermediate pixel data processing • Source image should be partitioned considering

window layouts of all endpoints.• The number of available multicast addresses limits

the number of partition.• Overhead incurred by multicast group membership

change increases window operation delay.• Multicasting over 10Gbit networks is very

expensive.

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Metric for Success and Timeline

• Metric for Success– Scalability, achievable bandwidth and latency– How successfully does this approach support

heterogeneous endpoints

• Timeline

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Task2006 2007

Aug Sep Oct Nov Dec Jan Feb Mar Apr May June

Preliminary Exam

First ImplementationPreparing SC Demo

CG&A paper

Scalable VersionHPDC paper

Full FunctionalityJPDC paper

Writing ThesisPreparing Defense


Experiment Plan and Equipment Needed

• Local test bed consists of a 28-node cluster, a 10Gbit switch and four-node SAGE Bridge

• The SAGE Bridge will be moved to StarLight for real-world test.

• Possible endpoints: Univ. of Michigan, Calit2/UCSD, SARA in Amsterdam and KISTI in Korea

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Conclusion

• I propose Visualcasting – a scalable real-time image distribution service for ultra-high resolution display environments.

• Visualcasting extends SAGE to support distant collaboration with multiple endpoints.

• Scalability, achievable bandwidth and latency as Visualcasting supports heterogeneous endpoints will determine the success of this approach.

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Documents

Byungil Jeong