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High performance distributed simulation for interactive simulated vascular reconstruction

High performance distributed simulation for interactive simulated vascular reconstruction. Robert G. Belleman and Roman Shulakov Section Computational Science, University of Amsterdam, Kruislaan 403, 1098 SJ Amsterdam, the Netherlands. Email: (robbel|rshulako)@science.uva.nl. Overview.

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High performance distributed simulation for interactive simulated vascular reconstruction

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  1. High performance distributed simulation for interactive simulated vascular reconstruction Robert G. Belleman and Roman Shulakov Section Computational Science, University of Amsterdam,Kruislaan 403, 1098 SJ Amsterdam, the Netherlands.Email:(robbel|rshulako)@science.uva.nl

  2. Overview • Interactive distributed simulation • Simulated vascular reconstruction in a virtual environment • Performance issues • Communication between distributed components • Results • Conclusions

  3. Interactive distributed simulation • Human in the loop experimentation • interactive exploration of data/parameter spaces

  4. Performance issues • Asynchronous, pipelined configuration • Distributed to exploit specialised resources • To increase performance: • decrease component execution times • shorten delays

  5. Vascular reconstruction • “Traditional” treatment

  6. Vascular reconstruction

  7. Simulated vascular reconstruction • Simulated treatment planning

  8. Simulated vascular reconstruction • Components: • blood flow simulation • visualization in a virtual environment • interaction with simulation and visualization (treatment planning) • Requirements: • Interactive system; fast response, fast update rate

  9. Fluid flow simulation • Lattice Boltzmann Method (LBM) • Lattice based particle method • Regular lattice, similar to CT or MRI datasets • Allows irregular 3D geometry • Allows changes at run-time • Velocity, pressure and shearstress calculated fromparticle densities • Non-compressiblehomogeneous fluid,laminar flow • Spatial and temporal locality • Ideal for parallelimplementation

  10. Fluid flow simulation • Lattice Boltzmann Method (LBM) • Lattice based particle method • Regular lattice, similar to CT or MRI datasets • Allows irregular 3D geometry • Allows changes at run-time • Velocity, pressure and shearstress calculated fromparticle densities • Non-compressiblehomogeneous fluid,laminar flow • Spatial and temporal locality • Ideal for parallelimplementation

  11. Parallel fluid flow simulation • Performance indication:

  12. Communication delay • 10-100Mb of data per iteration • Velocity, pressure, shear stress • May take seconds to transfer • Increasing communication throughput • Latency hiding • Decrease latency: faster response time, increased update rate • Payload reduction • Less data: shorter transfer times

  13. Latency hiding • Multiple connections • Waiting for acknowledgements is hidden • Packets can travel through different routes • Uses CAVERN

  14. Payload reduction • Data encoding • Decreases level of detail • Accuracy determined by type of representation • Lossy compression • Must be used with care! • Induces latency • “Standard” compression libraries (zlib) • Reduction to 10% not uncommon • Lossless compression • Induces latency

  15. Communication pipeline • Cascade of encoding, compression and multiple socket connections

  16. Results • Latency hiding:

  17. Results • Communication throughput:

  18. Conclusions • Increased throughput • In some cases over 5 times bandwidth • Time/location(/device?) independent interactive simulation • Access to high performancecomputing resources fromlow bandwidth connections • Use anywhere, at any time…onanything?

  19. Future work • Automatic tuning of communication stages • Intelligent interaction and presentation • Grid enabling: • Provide access to distributed resources • algorithms, scanners, databases • CAVERN can be used on Globus

  20. Thanks! • UvA, Section Computational Science • Sloot, Hoekstra, Zhao, Artoli, Merks, Shamonin • Leiden University Medical Center (LUMC) • LKEB (Reiber, vd Geest, Schaap) • Stanford University • Biomedical department (Zarins, Taylor) • SARA Computing and Networking Services See also in ICCS 2002 proceedings: • Lattice Boltzmann flow simulation: Artoli, Hoekstra (UvA/SCS) • Segmentation of MRA images: Schaap (LUMC) • Agent based solutions to interactive systems: Zhao (UvA/SCS)

  21. Treatment planning • Interactive planning in VR

  22. Treatment planning • Generate grids from analytical models

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