Intuitive Problem Solving: The Polder Meta Computing Inititiative Peter Sloot Computational Science University of Amsterdam, The NetherlandsSlide 2
Ariadneâs Red-Rope From PSE to Virtual Laboratory and Motivation Architecture Infrastructure Job Level: Hierarchical Scheduling Resource administration: Task-relocation Interaction && Case usage Interactive AlgorithmsSlide 3
Virtual Laboratory Environment Advanced Scientific Domains Computational Physics System Engineering Computational Bio-pharmaceutical Local User Local User Virtual Simulation & Exploration Environment (ViSE) Communication & coordinated effort (ComCol) Virtual-lab Information Management for Cooperation (VIMCO) Physical mechanical assembly Distributed Computing & Gigabit Local Area Network ViSE Net Client App. Client MRI/CT Internet 2 Wide Area NetworkSlide 4
Interactive Computing: Why? Objective: From Data, through Information to Knowledge Complexity: Huge information sets, complex procedures Approach: Parametric investigation and affectability examinations: Combine crude (tactile) information with recreation Person on top of it: Sensory communication Intelligent easy routes.:Slide 5
Intro: Case study from biomedicine...Slide 6
In Vitro In Vivo In Silico Changing the ParadigmSlide 7
In Vitro In Vivo In Silico Changing the ParadigmSlide 8
In Vitro In Vivo In Silico Changing the ParadigmSlide 9
Diagnosis & Planning Treatment Observation Current SituationSlide 10
Fast, High-throughput Low Latency Internet High Performance Super Computing New Possibilities in the VL Time and Space Independence 3D Information Simulation based arranging Surgeon âin the loopâSlide 11
Experimental set-upSlide 12
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 framework Host: The DAS 24 hub parallel group in a 200 hub wide range machine 200 MHz Pentium Pro Myrinet 150MB/s ATM wide-territory interconnect between bunchesSlide 14
Immersive EnvironmentsSlide 15
3D Information and InteractionSlide 16
Problem: Curse of elements: Static undertaking burden Dynamic assignment load Static errand allotment Predictable reallocation Dynamical reallocation Static asset load Dynamic asset loadSlide 17
Solution To Curse Performance of a parallel program typically managed by slowest assignment Task asset prerequisites and accessible assets both differ progressively Therefore, ideal errand designation changes Gain must surpass expense of relocation Resources utilized by long-running projects may be recovered by proprietorSlide 18
Node A Node B PVM undertaking 1 PVMD A PVMD B Node C PVM assignment 2 PVMD C Dynamite Initial State Two PVM undertakings conveying through a system of daemons Migrate assignment 2 to hub BSlide 19
Node A Node B New setting PVM assignment 1 PVMD A PVMD B Node C Program PVM Ckpt PVMD C Prepare for Migration Create new setting for undertaking 2 Tell PVM daemon B to expect messages for assignment 2 Update steering tables in daemons (first B, then A, later C)Slide 20
Checkpointing Node A Node B New setting PVM assignment 1 PVMD A PVMD B Node C Program PVM Ckpt PVMD C Send checkpoint sign to assignment 2 Flush associations Checkpoint undertaking to plateSlide 21
Cross-bunch checkpointing (outline) Node A Node B Helper assignment PVM errand 1 PVMD A PVMD B Node C Program PVM Ckpt PVMD C Send checkpoint sign to undertaking 2 Flush associations, close records Checkpoint assignment to circle by means of aide undertakingSlide 22
Restart Execution Node A Node B New PVM assignment 2 PVM errand 1 PVMD A PVMD B Node C PVMD C Restart checkpointed undertaking 2 on hub B Resume interchanges Re-open & re-position documentsSlide 23
Special contemplations Preserve correspondence PVM ought to keep on running as though nothing happened Use area free tending to Open records Preserve open record stateSlide 24
Performance Migration speed to a great extent reliant on the pace of shared record framework and that depends generally on the system NFS more than 100 Mbps Ethernet 0.4 s < T mig < 15 s for 2 MB < size img < 64 MB Communication pace diminished because of included overhead 25% for 1 byte direct messages 2% for 100 KB circuitous messagesSlide 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 bolstered and tried Pam-Crash (ESI) - auto accident reenactments CEM3D (ESI) - electro-magnetics code Grail (UvA) - vast, straightforward FEM code NAS parallel benchmarks BloodFlow MPI and attachment (Univ. of Krakow) libraries accessible Scheduling not yet agreeableSlide 26
Architecture: RevisitedSlide 27
Design Considerations High Quality presentation High Frame rate Intuitive cooperation Real-time reaction Interactive Algorithms High execution registering and systems administration...Slide 28
Problem: Time, time what has happened to us?Slide 29
Solution: AsynchronicitySlide 30
A cop to control the nonconcurrent formsSlide 31
Runtime Support Need bland system to bolster modalities Need interoperability High Level Architecture (HLA): information dispersion crosswise over heterogeneous stages adaptable characteristic and proprietorship components propelled time administrationSlide 32
Provoking a bitâ¦ Progress in common sciences originates from dismembering things ... Progress in software engineering originates from uniting things...Slide 33
Proof is in the pudding... Analytic Findings Occluded right iliac conduit 75% stenosis in left iliac supply route Occluded left SFA Diffuse illness in right SFASlide 34
Problem: From Image to Simulation MR Scan of Abdomen MR Scan of LegsSlide 35
Solution: 3DManual introduction Place begin point Place one or more end focuses Wave proliferates from begin to end point Backtrack = first estimation of the centerline Wave spreads from âcenterlineâ ï vessel divider Distance Transform from vessel divider to focus ï centerlineSlide 36
Wavefront Propagation Place begin point Place one or more end focuses Wave engenders from begin to end point Backtrack = first estimation of the centerline Wave proliferates from âcenterlineâ ï vessel divider Distance Transform from vessel divider to focus ï centerlineSlide 37
MRA: Backtrack Place begin point Place one or more end focuses Wave engenders from begin to end point Backtrack = first estimation of the centerline Wave spreads from âcenterlineâ ï vessel divider Distance Transform from vessel divider to focus ï centerlineSlide 38
MRA: Wavefront Propagation Place begin point Place one or more end focuses Wave proliferates from begin to end point Backtrack = first estimation of the centerline Wave engenders from âcenterlineâ ï vessel divider Distance Transform from vessel divider to focus ï centerlineSlide 39
MRA: Distance Transform Place begin point Place one or more end focuses Wave spreads from begin to end point Backtrack = first estimation of the centerline Wave engenders from âcenterlineâ ï vessel divider Distance Transform from vessel divider to focus ï centerlineSlide 40
3-D determination of locale of interestSlide 41
Tracking the vesselsSlide 42
Building the Geometric ModelsSlide 43
Alternate Treatments Preop AFB w/E-S Prox. Anast. AFB w/E-E Prox. Anast. Angio w/Fem-Fem Angio w/Fem-Fem & Fem-PopSlide 45
Problem: Flow through complex geometry After deciding the vascular structure mimic the blood-stream and weight dropâ¦ Conventional CFD strategies may fall flat: Complex geometry Numerical insecurity wrt connection Inefficient shear-stress countSlide 46
Solution to intelligent stream reproduction Use Cellular Automata as a mesoscopic model framework: Simple neighborhood cooperation Support for genuine material science and heuristics Computational proficientSlide 47
Mesoscopic Fluid Model Fluid model with Cellular Automata rules Collision: particles reshuffle speeds Imposed Constraints Conservation of mass Conservation of energy Isotropy Details...Slide 48
...Equivalence with NS For cross section with enough symmetry: proportional to the ceaseless incompressible Navier-Stokes mathematical statements: Implicit parallel and complex geometry support.Slide 49
Efficient Calculation of Shear-Stress Perpendicular force exchange: AND the energy stress tensor P that is directly identified with the shear stresses s stomach muscle From LBE plan:Slide 50
10 cm/sec 0 cm/sec Velocity MagnitudeSlide 51
Peak Systolic Pressures - Rest 150 mmHg 50 mmHg Preop AFB w/E-S Prox. Anast. AFB w/E-E Prox. Anast. Angio w/Fem-Fem Angio w/Fem-Fem & Fem-PopSlide 52
â¦ last slides...Slide 53
Other Virtual Laboratory Applications @ UvA Computing in Physics Computing in Engineering Computing in Engineering Bio-medicinal Computation Bio-informatics Environment Cultural Inheritance Environment VL for Material Science Traffic Payment for portability Apply VL in non-nature of administration environment Study of blood course through veins DNA Research Art objects safeguarding reclamation Meta information Integration Combining critical thinking & information serious situations Modeling VL in non-QoS circumstance environment Integration of reenactment & perception by man on the up and up Combing information mining & keen information bases Collaborative information incorporation User Central-part Central-part Virtual Laboratory Virtual Laboratory ViSE ComCol VIMCO Physical Apparatus Internet and Web Software Internet and Web Software Distributed Computer base Distributed Computer baseSlide 54
Acknowledgments RUL/AZL: H.
Propelled Critical thinking Frameworks: Arranging to take care of nontrivial issues, it is vital ...
Tony Fisher, Principal Examiner (GCSE Pilot and Functional Maths) ... Useful abilities capabilit ...
A couple of hundreds to have speedier seek than Manhattan. We utilized a preparation set of 2500 ...
A meta-investigation of the impact of Common Currencies on International Trade . Andrew k. Rose ...
Play the amusement: http://www.mazeworks.com/hanoi/Solving most diversions includes look ... Fou ...
2) A Proposed Solution Strategy: Logic Meta Programming ... Use rationale meta programming to. e ...
Key Critical thinking and Choice Making. Approaches for Conveying Vital Center to Hierarchical C ...
Executive of the Texas Advanced Computing Center (TACC) at the University of Texas. Some time ag ...
The Boston metro illustration proceeded. To speak to a chart characterizing a metro (see book, f ...
Critical thinking Agent. Critical thinking Agent. . Sample: Route finding. Occasion Planning. On ...
Goals. Clarify the four phases of issue solvingDescribe the red/green methods of issue solvingId ...
UNC Chapel Hill. S. Redon - M. C. Lin. Plot. Calculation overviewComputing obliged increasing sp ...
Problem Solving. Problem Solving. Initial State. Most everything we “do” can be consid ...
2. Intuitive Applications (CLI). An intuitive project with a charge line interface contains an a ...
The Two Cultures. Story. Examination Questions of Computing EducationStory 1: Teaching Computing ...
. Critical thinking includes settling on a progression of choices: choosing that something isn't ...
Targets . Figure out how to state and clear up a problemDevelop a method for issue solvingLearn ...
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...