Electronic Visualization Laboratory, University of Illinois at Chicago Collaborative Visualization Architecture in Scalable Adaptive Graphics Environment Byungil Jeong

Electronic Visualization Laboratory, University of Illinois at Chicago Introduction Data intensive domains need to visualize huge amount of data Data storage, computation and visualization resource Networking cost down => Sharing remote computation and visualization resource and data storage => The fundamental premise behind shared cyber-infrastructure OptIPuter: designing advanced cyber-infrastructure for data-intensive science using optical networks User requirements - Visualize large data in real-time using remote visualization resources - Seeing and interacting multiple high-resolution visualization at a time - Distant collaboration in high-resolution display environment Scalable Adaptive Graphics Environment (SAGE) : a specialized visualization middleware to support these user requirements - 2 -

Electronic Visualization Laboratory, University of Illinois at Chicago Scalable Adaptive Graphics Environment (SAGE) Remote laptop High-resolution maps Live video feeds 3D surface rendering Volume Rendering Remote sensing Information must be able to flexibly move around the wall

Electronic Visualization Laboratory, University of Illinois at Chicago SAGE Rendering Model - 4 -

Electronic Visualization Laboratory, University of Illinois at Chicago SAGE Demonstration in EVL LambdaVision: 11x5 tiled display, 100 Mega-pixel resolution Using ultra high-speed networks (multi-ten gigabits/sec) - 5 -

Electronic Visualization Laboratory, University of Illinois at Chicago User Driven Features of SAGE Sharing remote visualization resource and data –Reducing visualization cost –Securely keeping original data in the visualization center Unified visualization environment –Any visualization application can be integrated into SAGE –Simple pixel streaming API (10 to 20 lines of codes) Scalability in performance and resolution Fully multitasking environment on scalable displays –Scalable displays have been typically used for a single application –Concurrently run multiple visualization applications resizing and repositioning their windows - 6 -

Electronic Visualization Laboratory, University of Illinois at Chicago Comparison with Other Approaches SAGEDCVSGEDMXChromium Multi-tasking (window operation) Y DMX YY Display-rendering decoupling Y Y (RVN) Y -- Scalable parallel application support YYY - Y High-bandwidth wide-area streaming Y ---- Image multicasting for scalable tiled displays Y

Electronic Visualization Laboratory, University of Illinois at Chicago render1 dispNdisp2disp1 SAGE Architecture SAGE UI App Tiled Display FreeSpace Manager SAIL SAGE Receiver SAGE Receiver SAGE Receiver SAGE UI Pixel Stream SAGE MessagesSynchronization Channel SAIL: SAGE Application Interface Library render2renderM SAIL App

Electronic Visualization Laboratory, University of Illinois at Chicago How to manage dynamic pixel streams? Render A Render B AB Reconfigure Receiving Buffer Texture Rectangle Connect Reconfigure Image Partition Active Connection

Electronic Visualization Laboratory, University of Illinois at Chicago How to manage dynamic pixel streams? FSManager SAIL SAGE Receiver Pixel Data Control FSManager SAIL SAGE Receiver Pixel Data Control Obvious Approach (100 to 1000ms latency) –SAGE Receiver doesn’t know when newly partitioned data arrives –FSManager has to pause pixel streams for reconfiguration Improved Approach (10 to 100ms latency) –SAIL sends control data together with pixel data –Reconfigure streams without stopping streaming Obvious ApproachImproved Approach

Electronic Visualization Laboratory, University of Illinois at Chicago How to Partition Pixel Data? Pixel block streaming: independent of window layouts Good to stream pixels to multiple end-points Small sized pixel block Increases system calls for network send and pixel downloading Aggregation of pixel blocks Streamer SAGE Display Pixel Block Streaming Streamer SAGE Display Image Frame Streaming

Electronic Visualization Laboratory, University of Illinois at Chicago Synchronization of Dynamic Streams Each application has different refresh rate => has to be synchronized independently Groups of active or inactive nodes: dynamically changed Using low latency TCP channel for sending sync signals Active Nodes Inactive Nodes Sync Group A Sync Group B

Electronic Visualization Laboratory, University of Illinois at Chicago Network Streaming Protocol TCP module: low or unstable performance on long round- trip time wide-area networks (10Gbit networks) UDP module: no data flow control => packet loss, artifacts Extended the UDP module to control the data transfer rate not exceeding user definable upper bound Packet loss was reduced below 1%

Electronic Visualization Laboratory, University of Illinois at Chicago SAGE Users They want to collaborate each other using SAGE

Electronic Visualization Laboratory, University of Illinois at Chicago Visualcasting Support distant collaboration with multiple endpoints –All participants interact with one another, as well as with their data –Various display configuration at each endpoint Increasing the complexity of the pixel routing problem –Independent application layout at each endpoint –Dynamic changes in the number of applications and endpoints

Electronic Visualization Laboratory, University of Illinois at Chicago SAGE extended by Visualcasting SAGE only multiple sources single destination SAGE with Visualcasting multiple to multiple

Electronic Visualization Laboratory, University of Illinois at Chicago SAGE Bridge A new software component of SAGE Broadcasts pixel data to the endpoints Running on a high- performance PC cluster Placed at core hubs in the network

Electronic Visualization Laboratory, University of Illinois at Chicago How to Distribute Pixels to Multiple Endpoints? Sending Side (Overloaded) Rendering DuplicationPartitionDisplay Endpoints 4Mpix 1Gbps 10Mpix 10Gbps

Electronic Visualization Laboratory, University of Illinois at Chicago How to Distribute Pixels to Multiple Endpoints Sending Side Rendering DuplicationPartitionDisplay Endpoints SAGE Bridge 10Mpix 10Gbps 4Mpix 1Gbps Regrouping

Electronic Visualization Laboratory, University of Illinois at Chicago Visualcasting at SC06 EVL(Calit2/UCSD), SARA(Dutch Consortium), University of Michigan(Research Channel)

Electronic Visualization Laboratory, University of Illinois at Chicago Performance Evaluation Two 28-node LambdaVision cluster in San Diego (UCSD) and Chicago (UIC) National LambdaRail provided a 10gigabit dedicated optical network link between two cluster (CAVEWave) Gigabit Ethernet (GigE) network interface fully connected to each other through a gigabit network switch Excluding other traffic on the network Jumbo frames are used (9KB data packet size) UIC UCSD

Electronic Visualization Laboratory, University of Illinois at Chicago SAGE Streaming Performance A high-resolution image viewer, streaming from San Diego to Chicago over 10Gbit dedicated link, UDP, 0.9Gbps upper bound Over 80% of network utilization Scalability in frame rate and resolution

Electronic Visualization Laboratory, University of Illinois at Chicago SAGE Bridge Performance Total output bandwidth of a SAGE Bridge node 1Mpixel images up to nine endpoints Two dual core AMD 64bit CPU, a Myricom 10Gbit network interface Supported up to 7 endpoints without loosing scalability.

Electronic Visualization Laboratory, University of Illinois at Chicago Current Research 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. No additional node available: request senders to down- sample or compress pixel blocks What are the conditions for adding or removing SAGE Bridge nodes? How to support heterogeneity of endpoints in network bandwidth, computing power and display resolution?

Electronic Visualization Laboratory, University of Illinois at Chicago Conclusion Wide-area distributed visualization is now feasible at ultra- high resolutions while maintaining interactivity. SAGE Visualcasting supports global collaboration with multiple endpoints in scalable display environments. Scalability and heterogeneous endpoint support of Visualcasting

Electronic Visualization Laboratory, University of Illinois at Chicago Acknowledgement Support from the National Science Foundation: OptIPuter project, cooperative agreement OCI to University of California, San Diego; MRI - LambdaVision Thanks to Jason Leigh, Andrew Johnson, Luc Renambot, Tom Defanti Thanks for excellent support of EVL support team

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