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

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2 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. 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

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3 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Virtual Laboratory Environment Internet 2 Wide Area Network ViSE Net ClientApp. User MRI/CT Distributed Computing & Gigabit Local Area Network Local User Physical apparatus Virtual-lab Information Management for Cooperation (VIMCO) Communication & collaboration (ComCol) Virtual Simulation & Exploration Environment (ViSE) Advanced Scientific Domains Computational Physics System Engineering Computational Bio-medicine

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4 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. 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

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5 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Intro: Case study from biomedicine...

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6 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Changing the Paradigm In Vivo In Vitro In Silico

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7 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Changing the Paradigm In Vivo In Vitro In Silico

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8 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Changing the Paradigm In Vivo In Vitro In Silico

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9 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Current Situation Observation Diagnosis & Planning Treatment

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10 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. New Possibilities in the VL Fast, High-throughput Low Latency Internet High Performance Super Computing Time and Space Independence 3D Information Simulation based planning Surgeon ‘in the loop’

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11 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Experimental set-up

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12 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Architecture

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13 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. 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 GRAPE1GRAPE0 ATM Origine 2000 Cave

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14 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Immersive Environments

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15 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. 3D Information and Interaction

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16 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Problem: Curse of dynamics: Static task loadDynamic task load Static resource loadDynamic resource load Static task allocation Predictable reallocation Dynamical reallocation

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17 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Solution To Curse –Performance of a parallel program usually dictated 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

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18 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Dynamite Initial State Two PVM tasks communicating through a network of daemons Migrate task 2 to node B Node ANode B Node C PVMD A PVMD B PVMD C PVM task 1 PVM task 2

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19 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. 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) Node ANode B Node C PVMD A PVMD B PVMD C PVM task 1 Program PVM Ckpt New context

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20 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Checkpointing Send checkpoint signal to task 2 Flush connections Checkpoint task to disk Node ANode B Node C PVMD A PVMD B PVMD C PVM task 1 Program PVM Ckpt New context

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21 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Cross-cluster checkpointing (design) Send checkpoint signal to task 2 Flush connections, close files Checkpoint task to disk via helper task Node ANode B Node C PVMD A PVMD B PVMD C PVM task 1 Program PVM Ckpt Helper task

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22 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Restart Execution Restart checkpointed task 2 on node B Resume communications Re-open & re-position files Node ANode B Node C PVMD A PVMD B PVMD C PVM task 1 New PVM task 2

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23 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Special considerations –Preserve communication PVM should continue to run as if nothing happened Use location independent addressing –Open files Preserve open file state

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24 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. 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 < T mig < 15 s for 2 MB < size img < 64 MB –Communication speed reduced due to added overhead 25% for 1 byte direct messages 2% for 100 KB indirect messages

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25 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Current status: Dynamite Part –Checkpointer operational under Solaris and higher (UltraSparc, 32 bit) Linux/i 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

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26 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Architecture: Revisited

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27 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Design Considerations –High Quality presentation –High Frame rate –Intuitive interaction –Real-time response –Interactive Algorithms –High performance computing and networking...

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28 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Problem: Time, time what has become of us?

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29 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Solution: Asynchronicity

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30 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. A police officer to guide the asynchronous processes

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31 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. 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

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32 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Provoking a bit… Progress in natural sciences comes from taking things apart... Progress in computer science comes from bringing things together...

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33 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. 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

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34 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Problem: From Image to Simulation MR Scan of AbdomenMR Scan of Legs

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35 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Solution: 3DManual initialization Place start point Wave propagates from start- to end point Place one or more end points Backtrack = first estimation of the centerline Distance Transform from vessel wall to center centerline Wave propagates from ‘centerline’ vessel wall

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36 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Wavefront Propagation Place start point Wave propagates from start- to end point Place one or more end points Backtrack = first estimation of the centerline Distance Transform from vessel wall to center centerline Wave propagates from ‘centerline’ vessel wall

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37 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. MRA: Backtrack Place start point Wave propagates from start- to end point Place one or more end points Backtrack = first estimation of the centerline Distance Transform from vessel wall to center centerline Wave propagates from ‘centerline’ vessel wall

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38 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. MRA: Wavefront Propagation Place start point Wave propagates from start- to end point Place one or more end points Backtrack = first estimation of the centerline Distance Transform from vessel wall to center centerline Wave propagates from ‘centerline’ vessel wall

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39 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. MRA: Distance Transform Place start point Wave propagates from start- to end point Place one or more end points Backtrack = first estimation of the centerline Distance Transform from vessel wall to center centerline Wave propagates from ‘centerline’ vessel wall

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40 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. 3-D selection of region of interest

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41 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Tracking the vessels

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42 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Building the Geometric Models

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43 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. VR-Interaction

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44 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Alternate Treatments Angio w/ Fem-Fem & Fem-Pop AFB w/ E-S Prox. Anast. Angio w/ Fem-Fem AFB w/ E-E Prox. Anast. Preop

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45 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. 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

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46 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Solution to interactive flow simulation –Use Cellular Automata as a mesoscopic model system:Cellular Automata Simple local interaction Support for real physics and heuristics Computational efficient

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47 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Mesoscopic Fluid Model –Fluid model with Cellular Automata rules –Collision: particles reshuffle velocities –Imposed Constraints Conservation of mass Conservation of momentum Isotropy Details...

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48 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands....Equivalence with NS –For lattice with enough symmetry: equivalent to the continuous incompressible Navier-Stokes equations: Implicit parallel and complex geometry support.

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49 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Efficient Calculation of Shear-Stress AND the momentum stress tensor that is linearly related to the shear stresses Perpendicular momentum transfer: From LBE scheme:

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50 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Velocity Magnitude 10 cm/sec 0 cm/sec

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51 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Peak Systolic Pressures - Rest 150 mmHg 50 mmHg Angio w/ Fem-Fem & Fem-Pop AFB w/ E-S Prox. Anast. Angio w/ Fem-Fem AFB w/ E-E Prox. Anast. Preop

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52 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. … last slides...

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53 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Internet and Web Software Distributed Computer infrastructure Central-part Virtual Laboratory Physical Apparatus User ViSEComColVIMCO Internet and Web Software Distributed Computer infrastructure Central-part Virtual Laboratory User Computing in Physics VL for Material Science ViSEComColVIMCO Meta data Integration Computing in Engineering Traffic Payment for mobility Combining problem solving & data intensive environments Bio-medical Computation Study of blood flow through veins Integration of simulation & visualization by man in the loop Bio- informatics Environment DNA Research Combing data mining & intelligent data bases Cultural Inheritance Environment Art objects preservation restoration Collaborative data integration Computing in Engineering Apply VL in non- quality of service environment Modeling VL in non-QoS situation environment Other Virtual Laboratory UvA

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54 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Acknowledgements Stanford: Charley Taylor, PhD. Christopher K. Zarins, PhD. M.D. UvA: Robert Belleman Alfons Hoekstra, PhD Dick van Albada, PhD Benno Overeinder, PhD Krakow Marian Bubak, PhD Kamil Iskra RUL/AZL: H. Reiber, PhD. Bloem, PhD, M.D. SARA: A. de Koning, PhD. Arcobel: S. ten Den IBM: J. Geise

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55 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Support ICES-KIS-1 ICES-KIS-2 KNAW NWO/FOM IBM SARA SGI Platform HPCN

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56 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands.

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57 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands.

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58 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands MFlop/s IBM 704 CDC 6600 Cray X-MP Cray Y-MP CM-5 ASCI-Red ASCI-Blue 2D Plasma 48 hr Weather 72 hr Weather Pharmaceutical ? Structural Biology Oil reservoir

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59 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Results - Mean Flow Rates (ml/min) - Rest

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60 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Cellular Automata –1966 Introduced by John von Neumann –1985 Stephen Wolfram suggested CA are capable of Universal Computation –1990 Lindgren et al., proved UC in 1D CA

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61 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands t= t= Productie Regel 110

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Time Evolution of 1D Cellular Automata 110 BackBack to Mesoscopic Models

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63 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. The Lattice Gas model –Fluid model with Cellular Automata rules –Collision: particles reshuffle velocities –Imposed Constraints Conservation of mass Conservation of momentum Isotropy

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64 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. The Hexagonal Lattice

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65 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Collision rules examples Two body collision N1 AND N4 => N2 AND N5 && N3 AND N6 Three body collision N2 AND N4 AND N6 => N1 AND N3 AND N5

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Streaming and Colliding

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67 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. From LGA to LBM –Average LGA equation to get continuous values instead of boolean values –Boltzmann molecular chaos assumption to factorize products in collision operator: => Iterate:

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68 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. From Micro Dynamics to Macro Dynamics (1) –Taylor expansion to get continuous differential operators:

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69 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. From Micro Dynamics to Macro Dynamics (2) –Chapman Enskog expansion of equilibrium Distribution Function: –With imposed constraints:

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70 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. From Micro Dynamics to Macro Dynamics (3) –Multi-scale expansion of time and space derivatives: –Solve collision/flow equation for different order of

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71 Peter Sloot: Computational Science, University of Amsterdam, The Netherlands. Back to mesoscopic models

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