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Distributed Visualization Applications for Dynamic Data Movement

Explore a decentralized infrastructure supporting geographically distributed users with shared infrastructure, allowing for data replication and movement in Distributed Visualization Applications. Introducing novel concepts like dynamic data movement for optimized resource performance.

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Distributed Visualization Applications for Dynamic Data Movement

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  1. 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

  2. Distributed Visualization

  3. Applications LoRS:runtime system L-Bone exNode IBP Physical Layer Data Replication: exNodes D4 D2 D3 D1 F1 F2 F3

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

  5. 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 !!!

  6. 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”

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

  8. 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

  9. 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

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