Distributed Visualization Our use of this term extends its traditional meaning –Distributed: Still aim to support geographically distributed users and.

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Presentation transcript:

Distributed Visualization Our use of this term extends its traditional meaning –Distributed: Still aim to support geographically distributed users and collaborative communities –Decentralized infrastructure: The infrastructure does not need to be centralized as in “compute” centers –Shared infrastructure: The comp/storage nodes can be independent Internet computers

Distributed Visualization

Data Replication: exNodes D1D1 D2D2 D3D3 D4D4 F1F2 F3 Physical Layer IBP LoRS:runtime system Applications L-BoneexNode

Network Functional Unit Input Allocation Output Allocation IBP Memory mapping MEM PE RD RD/WR NFU is novel due to: 1.weakened semantics and 2.control of security-sensitive operations.

Scheduling Depots: {P1,P2,…,Pm} Pi described by bw bi & computational power ci Partitioned dataset {d1,d2,…, dn}, k-way replication Vis only need one copy of each dj (Optional) DM tasks Mij replicates dj on Pi Key Challenge: Resource performance varies over time !!!

Dynamic Data Movement Some data partitions are just “unlucky” to be on slow or heavily loaded servers After fast depots are done with local tasks, can dynamically “steal” some slow “partitions”

Results: the depots Most depots used running on Planet-Lab Workloads varied across servers and time Realistic test of feasibility on shared, decentralized infrastructure

Results: the data Test data: 30 timestep of Tera-scale Supernova Initiative, 75GB in total –Provided by Tony Mezzacappa (ORNL) and John Blondin (ORNL) under the auspices of DOE SciDAC TSI project

Results: the performance 800x800 image resolution, 0.5 step size in ray-casting, per-fragment classification and Phong shading With 100 depots, the average rendering time: 237 sec