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Interactive Problem Solving: The Polder Meta Computing Inititiative

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### Interactive Problem Solving:The Polder Meta Computing Inititiative

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

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

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

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’

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

Problem: Curse of dynamics:

Static task load

Dynamic task load

Static task allocation

Predictable reallocation

Dynamical reallocation

Static resource load

Dynamic resource load

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

Node B

PVMtask 1

PVMD

A

PVMD

B

Node C

PVMtask 2

PVMD

C

Dynamite Initial StateTwo PVM tasks communicating through a network of daemons

Migrate task 2 to node B

Node B

Newcontext

PVMtask 1

PVMD

A

PVMD

B

Node C

Program

PVM

Ckpt

PVMD

C

Prepare for MigrationCreate 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)

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

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

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

Special considerations

- Preserve communication
- PVM should continue to run as if nothing happened
- Use location independent addressing

- Open files
- Preserve open file state

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

Current status: Dynamite Part MPI and socket (Univ. of Krakow) libraries available Scheduling not yet satisfactory

- 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

Design Considerations

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

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

Provoking a bit…

Progress in natural sciences comes from taking things apart ...

Progress in computer science comes from bringing things together...

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

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

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

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

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

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

Building the Geometric Models

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

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

Solution to interactive flow simulation

- Use Cellular Automata as a mesoscopic model system:
- Simple local interaction
- Support for real physics and heuristics
- Computational efficient

Mesoscopic Fluid Model

- Fluid model with Cellular Automata rules
- Collision: particles reshuffle velocities
- Imposed Constraints
- Conservation of mass
- Conservation of momentum
- Isotropy

Details...

...Equivalence with NS

- For lattice with enough symmetry: equivalent to the continuous incompressible Navier-Stokes equations:

Implicit parallel and complex geometry support.

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:

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-Pop

Other Virtual Laboratory Applications @ UvA

Computing in Physics

Computing in

Engineering

Computing in Engineering

Bio-medical

Computation

Bio- informatics

Environment

Cultural Inheritance Environment

VL for Material Science

Traffic Payment for mobility

Apply VL in non-quality of service environment

Study of blood flow through veins

DNA

Research

Art objects

preservation

restoration

Meta data

Integration

Combining problem solving & data intensive environments

Modeling VL in non-QoS situation environment

Integration of simulation & visualization by man in the loop

Combing data

mining & intelligent

data bases

Collaborative data

integration

User

User

Central-part

Central-part

Virtual Laboratory

Virtual Laboratory

ViSE ComCol VIMCO

ViSE ComCol VIMCO

Physical Apparatus

Internet and Web Software

Internet and Web Software

Distributed Computer infrastructure

Distributed Computer infrastructure

Acknowledgements

RUL/AZL:

H. Reiber, PhD.

Bloem, PhD, M.D.

SARA:

A. de Koning, PhD.

Arcobel:

S. ten Den

IBM:

J. Geise

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

?

ASCI-Blue

1000.000

ASCI-Red

Structural Biology

CM-5

100.000

Pharmaceutical

10.000

72 hr Weather

Cray Y-MP

48 hr Weather

1000

Cray X-MP

2D Plasma

100

Oil reservoir

10

CDC 6600

1

IBM 704

0.1

1955

1965

1975

1985

1995

2005

Results - Mean Flow Rates (ml/min) - Rest

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

t=0

0

1

0

1

0

0

1

1

0

t=1

1

1

1

0

1

1

1

0

1

100

0

111

0

110

1

101

1

011

1

010

1

001

1

000

0

Time Evolution of 1D Cellular Automata 110

Back to Mesoscopic Models

The Lattice Gas model

- Fluid model with Cellular Automata rules
- Collision: particles reshuffle velocities
- Imposed Constraints
- Conservation of mass
- Conservation of momentum
- Isotropy

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

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:

From Micro Dynamics to Macro Dynamics (1)

- Taylor expansion to get continuous differential operators:

From Micro Dynamics to Macro Dynamics (2)

- Chapman Enskog expansion of equilibrium Distribution Function:
- With imposed constraints:

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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