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  1. Interactive Problem Solving:The Polder Meta Computing Inititiative Peter Sloot Computational Science University of Amsterdam, The Netherlands

  2. Ariadne’s Red-Rope • From PSE to Virtual Laboratory and Motivation • Architecture • Infrastructure • Job Level: Hierarchical Scheduling • Resource management: Task-migration • Interaction && Case implementation • Interactive Algorithms

  3. Virtual Laboratory Environment Advanced Scientific Domains Computational Physics System Engineering Computational Bio-medicine Local User Local User Virtual Simulation & Exploration Environment (ViSE) Communication & collaboration (ComCol) Virtual-lab Information Management for Cooperation (VIMCO) Physical apparatus Distributed Computing & Gigabit Local Area Network ViSE Net Client App. User MRI/CT Internet 2 Wide Area Network

  4. Interactive Computing: Why? • Goal: From Data, via Information to Knowledge • Complexity: Huge data-sets, complex processes • Approach: Parametric exploration and sensitivity analyses: • Combine raw (sensory) data with simulation • Person in the loop: • Sensory interaction • Intelligent short-cuts

  5. Intro: Case study from biomedicine...

  6. In Vitro In Vivo In Silico Changing the Paradigm

  7. In Vitro In Vivo In Silico Changing the Paradigm

  8. In Vitro In Vivo In Silico Changing the Paradigm

  9. Diagnosis & Planning Treatment Observation Current Situation

  10. Fast, High-throughput Low Latency Internet High Performance Super Computing New Possibilities in the VL • Time and Space Independence • 3D Information • Simulation based planning • Surgeon ‘in the loop’

  11. Experimental set-up

  12. Architecture

  13. Cave Origine 2000 9 10 11 12 13 14 8 15 7 16 6 17 5 18 4 19 ATM 3 20 2 1 0 23 22 21 GRAPE1 GRAPE0 Architecture Continued: Hybrid system • Host: The DAS • 24 node parallel cluster in a 200 node wide area machine • 200 MHz Pentium Pro • Myrinet 150MB/s • ATM wide-area interconnect between clusters

  14. Immersive Environments

  15. 3D Information and Interaction

  16. Problem: Curse of dynamics: Static task load Dynamic task load Static task allocation Predictable reallocation Dynamical reallocation Static resource load Dynamic resource load

  17. Solution To Curse • Performance of a parallel program usuallydictated by slowest task • Task resource requirements and available resources both vary dynamically • Therefore, optimal task allocation changes • Gain must exceed cost of migration • Resources used by long-running programs may be reclaimed by owner

  18. Node A Node B PVMtask 1 PVMD A PVMD B Node C PVMtask 2 PVMD C Dynamite Initial State Two PVM tasks communicating through a network of daemons Migrate task 2 to node B

  19. Node A Node B Newcontext PVMtask 1 PVMD A PVMD B Node C Program PVM Ckpt PVMD C Prepare for Migration Create new context for task 2 Tell PVM daemon B to expect messages for task 2 Update routing tables in daemons (first B, then A, later C)

  20. Checkpointing Node A Node B Newcontext PVMtask 1 PVMD A PVMD B Node C Program PVM Ckpt PVMD C Send checkpoint signal to task 2 Flush connections Checkpoint task to disk

  21. Cross-cluster checkpointing(design) Node A Node B Helper task PVMtask 1 PVMD A PVMD B Node C Program PVM Ckpt PVMD C Send checkpoint signal to task 2 Flush connections, close files Checkpoint task to disk via helper task

  22. Restart Execution Node A Node B NewPVM task 2 PVMtask 1 PVMD A PVMD B Node C PVMD C Restart checkpointed task 2 on node B Resume communications Re-open & re-position files

  23. Special considerations • Preserve communication • PVM should continue to run as if nothing happened • Use location independent addressing • Open files • Preserve open file state

  24. Performance • Migration speed largely dependent on the speed of shared file system • and that depends mostly on the network • NFS over 100 Mbps Ethernet • 0.4 s < Tmig < 15 s for 2 MB < sizeimg < 64 MB • Communication speed reduced due to added overhead • 25% for 1 byte direct messages • 2% for 100 KB indirect messages

  25. Current status: Dynamite Part • Checkpointer operational under • Solaris 2.5.1 and higher (UltraSparc, 32 bit) • Linux/i386 2.0 and 2.2 (libc5 and glibc 2.0) • PVM 3.3.x applications supported and tested • Pam-Crash (ESI) - car crash simulations • CEM3D (ESI) - electro-magnetics code • Grail (UvA) - large, simple FEM code • NAS parallel benchmarks • BloodFlow • MPI and socket (Univ. of Krakow) libraries available • Scheduling not yet satisfactory

  26. Architecture: Revisited

  27. Design Considerations • High Quality presentation • High Frame rate • Intuitive interaction • Real-time response • Interactive Algorithms • High performance computing and networking...

  28. Problem: Time, time what has become of us?

  29. Solution: Asynchronicity

  30. A police officer to guide the asynchronous processes

  31. Runtime Support • Need generic framework to support modalities • Need interoperability • High Level Architecture (HLA): • data distribution across heterogeneous platforms • flexible attribute and ownership mechanisms • advanced time management

  32. Provoking a bit… Progress in natural sciences comes from taking things apart ... Progress in computer science comes from bringing things together...

  33. Proof is in the pudding... • Diagnostic Findings • Occluded right iliac artery • 75% stenosis in left iliac artery • Occluded left SFA • Diffuse disease in right SFA

  34. Problem: From Image to Simulation MR Scan of Abdomen MR Scan of Legs

  35. Solution: 3DManual initialization Place start point Place one or more end points Wave propagates from start- to end point Backtrack = first estimation of the centerline Wave propagates from ‘centerline’  vessel wall Distance Transform from vessel wall to center  centerline

  36. Wavefront Propagation Place start point Place one or more end points Wave propagates from start- to end point Backtrack = first estimation of the centerline Wave propagates from ‘centerline’  vessel wall Distance Transform from vessel wall to center  centerline

  37. MRA: Backtrack Place start point Place one or more end points Wave propagates from start- to end point Backtrack = first estimation of the centerline Wave propagates from ‘centerline’  vessel wall Distance Transform from vessel wall to center  centerline

  38. MRA: Wavefront Propagation Place start point Place one or more end points Wave propagates from start- to end point Backtrack = first estimation of the centerline Wave propagates from ‘centerline’  vessel wall Distance Transform from vessel wall to center  centerline

  39. MRA: Distance Transform Place start point Place one or more end points Wave propagates from start- to end point Backtrack = first estimation of the centerline Wave propagates from ‘centerline’  vessel wall Distance Transform from vessel wall to center  centerline

  40. 3-D selection of region of interest

  41. Tracking the vessels

  42. Building the Geometric Models

  43. VR-Interaction

  44. Alternate Treatments Preop AFB w/ E-S Prox.Anast. AFB w/ E-E Prox.Anast. Angio w/Fem-Fem Angio w/ Fem-Fem & Fem-Pop

  45. Problem: Flow through complex geometry • After determining the vascular structure simulate the blood-flow and pressure drop… • Conventional CFD methods might fail: • Complex geometry • Numerical instability wrt interaction • Inefficient shear-stress calculation

  46. Solution to interactive flow simulation • Use Cellular Automata as a mesoscopic model system: • Simple local interaction • Support for real physics and heuristics • Computational efficient

  47. Mesoscopic Fluid Model • Fluid model with Cellular Automata rules • Collision: particles reshuffle velocities • Imposed Constraints • Conservation of mass • Conservation of momentum • Isotropy Details...

  48. ...Equivalence with NS • For lattice with enough symmetry: equivalent to the continuous incompressible Navier-Stokes equations: Implicit parallel and complex geometry support.

  49. Efficient Calculation of Shear-Stress Perpendicular momentum transfer: AND the momentum stress tensor P thatis linearly related to the shear stresses sab From LBE scheme:

  50. 10 cm/sec 0 cm/sec Velocity Magnitude