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The Jungle Universe
About scales and physics in the cosmos
Simon
Portegies Zwart
Sterrewacht Leiden
Observation of the early universe
(WMAP)
Abel1689
Stephen's quintuplet
The universe is multi­physics
The universe is multi­scale
Jungle scales
Size scale covers anythin from:
●
13.8 billion light years to km­size ●
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that covers 24 orders of magnitude
13.8 billion years to seconds
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that covers 18 orders of magnitude
jν
Sν =
k (ν )
F=
G m 1 m2
r2
dIν
= − Iν + Sν
dτ s
D u  ∇ p
2
=F−
ν ∇ u
Dt
ρ


Du ∂u 

=
+ ( u ⋅ ∇)u
Dt ∂t

∇⋅u = 0
∂P
Gm
=−
∂m
4π r 4
∂r
1
=
∂m 4π r 2 ρ ( P, T , Yi )
∂L
= ε nuc ( P, T , Yi ) + ε ν ( P, T , Yi ) + ε grav ( P, T , Yi )
∂m
∂T
GmT
=−
∇ ( P, T , Yi )
2
∂m
4π r P
Subrahanyan Chandrasekhar
Cloude­Louise Navier
James Clark Maxwell
George Gabriel Stokes
Sir Isaac Newton
Sir Arthur Eddington
Prehistoric computational astrophysics
Sumerian cuneform clay tablet
dated around 1,200BC
explaining the periodic behavior the planet Venus around 1,600BC
(compute speed ~ 1 FLOP)
Abacus (500BC, compute speed ~10FLOP)
”...'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question."
1960
von Neuman & IAS
2003
~30 000 000 times faster
500BC
Jun & GRAPE­4
Radiative transport
gas­dynamics
Maxwell equations
hydro­dynamics
Gravity
Stellar evolution
LGM
DAS­4
Computational challenges
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High performance (desktop) computing
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Distributed (wide area) computing
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Problem solving environments (software)
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Data acquisition
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Data mining
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Visualization
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Virtual collaboration
1908­2000
10mFlops
Software operated computers
Manchester mark1 (1948, 550 FLOPs)
Software by Tom Kilburn
The next generation problem solving environments
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Specialization (higher resolution)
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Optimization (high­performance)
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Diversification (wide range of applications)
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Hybridization (multi physics)
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Preservation (containment of existing codes)
The Astrophysical Multipurpose Software Environment AMUSE
http://amusecode.org
Scientific research and development team
●
Marco Spaans ●
Steve McMillan
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Gijs Nelemans
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Paul Groot
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Vincent Icke
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Eline Tolstoy
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Onno Pols
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Evert Glebbeek
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Lex Kaper
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Rien vd Weijgeart
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Rob Knop
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John Fregeau
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Breanndan O Nuaillan
AMUSE ­ philosophy
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Build on community codes
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Standarized interfaces
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Automate as much as possible
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Builds on lessons learned from previous generations
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Core Team: –
Inti Pelupessy (post­doc)
–
Arjen van Elteren (software engineer)
–
Marcel Marosvolgi, Nathan de Vries (programmers)
–
David Jansen (user support)
www.amusecode.org
AMUSE ­ design
Stellar Evolution
Hydrodynamics
Radiative Transfer
Gravity AMUSE
Combining existing codes
INPUT
OUTPUT
With an extensive support framework
To provide a generic framework
For doing astrophysical experiments
Compare models
Unit handling
Data conversion
Initial conditions
AMUSE http://amusecode.org
●
Python Script
Next Level
Particles
Units
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Legacy Interfaces
GD
HD
SE
RT
Message Channel
MPI
C/C++ code
Fortran Code
Layers having different roles
Written in C/C++, Java Fortran and Python
Pelupessy etal in prep
User script
Message passing script
Message passing source
Process 1
Community code
Process 2
Send request
evolve()
Send request
Send answer
Confirm request
evolve()
Send request
Evolve() done
Send answer
Confirm request
Confirm request
Confirm request
Two examples
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Formation of J1903+0327 (ApJ in press: ArXive:1103­2275)
–
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Gravitational dynamics + Stellar evolution
Evolution of young star cluster (to be submitted)
–
Gravitational dynamics + Stellar evolution + Hydro dynamics
Simulating Embedded star clusters
NGC3603 cluster
By HST
Numerical ingredients
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●
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Gravitational dynamics
–
Direct N­body integration (PhiGRAPE) –
GPU or GRAPE equipped pc
Stellar evolution
–
Henyey stellar evolution (MESA)
–
Beowulf computer cluster
Gas dynamics
–
Smoothed particles hydrodynamics (Fi)
–
Super computer
Evolution of a gas rich star cluster
SFE=0.05 ffb=0.1
SFE=0.50 ffb=0.01
AMUSE Today
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Automated referencing
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Unit conversion
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Online documentation
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Suite of examples
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Intricate module coupling via Hamiltonian splitting
Wish­list for AMUSE
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Runtime crash­recovery
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Self­consistent code restart
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Initial conditions repository
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Extensive data mining and analysis toolbox
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High­performance AMUSE
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AMUSE on the grid (PDRA Niels Drost VU)
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Asynchronous communication support
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Load balancing on heterogeneous architectures
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Data tunneling protocol