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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

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interactive problem solving the polder meta computing inititiative

Interactive Problem Solving:The Polder Meta Computing Inititiative

Peter Sloot

Computational Science

University of Amsterdam, The Netherlands

ariadne s red rope
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
slide3

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
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
new possibilities in the vl

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’
architecture continued hybrid system

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
problem curse of dynamics
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
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
dynamite initial state

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

prepare for migration

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)

checkpointing
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
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
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
Special considerations
  • Preserve communication
    • PVM should continue to run as if nothing happened
    • Use location independent addressing
  • Open files
    • Preserve open file state
performance
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
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
design considerations
Design Considerations
  • High Quality presentation
  • High Frame rate
  • Intuitive interaction
  • Real-time response
  • Interactive Algorithms
  • High performance computing and networking...
runtime support
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
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
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
problem from image to simulation
Problem: From Image to Simulation

MR Scan of Abdomen

MR Scan of Legs

solution 3dmanual initialization
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
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
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
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
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

alternate treatments
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
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
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
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
...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
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
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
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
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

support
Support

IBM

SARA

SGI

Platform HPCN

ICES-KIS-1

ICES-KIS-2

KNAW

NWO/FOM

slide56
http://science.uva.nl/~sloot

sloot@science.uva.nl

slide58

MFlop/s

?

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

cellular automata
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
slide61

Productie Regel 110

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

the lattice gas model
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
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
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
From Micro Dynamics to Macro Dynamics (1)
  • Taylor expansion to get continuous differential operators:
from micro dynamics to macro dynamics 2
From Micro Dynamics to Macro Dynamics (2)
  • Chapman Enskog expansion of equilibrium Distribution Function:
  • With imposed constraints:
from micro dynamics to macro dynamics 3
From Micro Dynamics to Macro Dynamics (3)
  • Multi-scale expansion of time and space derivatives:
  • Solve collision/flow equation for different order of 