OWLS: OverWhelmingly Large Simulations

Joop Schaye, Craig Booth, Claudio Dalla Vecchia, Alan Duffy, Marcel Haas, Volker Springel,

Tom Theuns, Luca Tornatore, Rob Wiersma, …

The formation of galaxies and the evolution of the intergalactic medium

Outline

1. Introducing cosmological simulations and OWLS

2. Preparing for and coping with lots of data

3. Data format and units

4. Microsimulations

5. Virtual observations of simulations

6. Discussion points

Example science questions• What determines the SF history of the

universe?• Where are the baryons and how can

they be detected?• Where are the metals and how can they

be detected?• How does galaxy formation depend on

environment?• What are the large-scale effects of

galactic winds driven by stars and AGN?• Virtual observations

Cosmological hydro simulations

• Evolution from z>~100 to z ~< 10 of a representative part of the universe

• Boundary conditions: periodic• Expansion solved analytically and scaled

out• Initial conditions from the CMB • Components: cold dark matter, gas, stars,

radiation (optically thin)• Scales ~< kpc to ~ 100 Mpc• Sub-grid modules are a crucial part of the

game• Discretizaton: time and mass (SPH)

The OWLS simulations• LOFAR IBM Bluegene/L (12k CPUs, PowerPC

440, 700 MHz, 500 MB per 2 CPUs)• Cosmological (default: WMAP3)• Hydrodynamical (SPH)• Gadget III• 2xN3 particles, N = 512 for most• Two sets:

– L = 25 Mpc/h to z=2 – L = 100 Mpc/h to z=0

• Runs repeated many times with varying physics/numerics

• Status: runs nearly done, starting analysis

OWLS subgrid physics improvements

• New star formation• New wind model• Added chemodynamics• New cooling

OWLS Numerical Resolution

• 25 Mpc/h (run to z=2):– m_bar = 1x10^6 Msun/h – Softening = 2 kpc/h comoving < 0.5 kpc/h proper

• 100 Mpc/h (run to z=0):– m_bar = 7x10^7 Msun/h– Softening = 8 kpc/h comoving

< 2.0 kpc/h proper

Gas density evolution

Gas density zoom

Zoom

CDV, OWLS project

Outline

1. Introducing cosmological simulations and OWLS

2. Preparing for and coping with lots of data

3. Data format and units

4. Microsimulations

5. Virtual observations of simulations

6. Discussion points

OWLS data volume

• 7 - 42 floats per particle up to 168 bytes/particle

• 268 M particles per snapshot 26 GB/snapshot

• Up to 35 snapshots per simulations 900 GB/simulation

• ~60 large simulations 54 TB of snapshot data

How do we analyze 50+ TB of data?

• First analyze lower resolution versions• Use hdf5 only read what is actually needed• Use fast visualization software (e.g. avoid SPH

interpolation)• Produce as much as possible on the fly:

– Logs (e.g. SF histories)– Grids (e.g. baryon distribution) – Sight lines (e.g. QSO absorption spectra)– Images (e.g. for videos)– Zooms saved on the fly (e.g. most massive object)– Diagnostic particle arrays (e.g. max temperature)– Group catalogues

Analysis that can be done on a notebook:

• Low-resolution versions• Evolution of globally averaged (or

gridded) quantities• Halo integrated properties• Halo profiles• Absorption spectra• Most things related to zooms

Outline

1. Introducing cosmological simulations and OWLS

2. Preparing for and coping with lots of data

3. Data format and units

4. Microsimulations

5. Virtual observations of simulations

6. Discussion points

Hdf5 – Hierarchical Data Format

• Binary fast I/O and compressed• Machine-independent• Intuitive hierarchical “directory-like”

structure• Individual elements can be read, modified

and saved • Easy to add meta-data• Utilities available to view the data in ascii

or graphical form (incl. tables, images)• Very easy to read/write from IDL, C, F90• Free

Lay-out of an hdf5 file

hdf5 groups

hdf5 data sets

Data format: Units• Data should use code units to

allow easy debugging, restarting• But data should be in physically

sensible units to allow analysis by external users

• Cosmological data should clarify h and aexp dependence for external users

Solution: use code units but includeconversion factors to cgs and aexp and hdependencies as meta-data.

Attributes to data

sets

Outline

1. Introducing cosmological simulations and OWLS

2. Preparing for and coping with lots of data

3. Data format and units

4. Microsimulations

5. Virtual observations of simulations

6. Discussion points

Outline

1. Introducing cosmological simulations and OWLS

2. Preparing for and coping with lots of data

3. Data format and units

4. Microsimulations

5. Virtual observations of simulations

6. Discussion points

Virtual Observations: Uses

• Guide the design of observational campaigns and instruments

• Test data analysis pipelines• Determine selection effects• Test theoretical models• PR/education

Virtual Observations: Types• Galaxy magnitudes

– Population synthesis, which depends on• Assumed IMF• Age and composition of star particle

– Dust column and assumed reddening law• Stellar light images• Absorption spectra• Gas emission maps (2-D: images, 3-D:

IFU datacubes)• Other maps: e.g. lensing, SZ, dust

absorption/emission

Creating Virtual Observations

• Compute emission and/or absorption of each resolution element

• Project and grid data, optionally combine different time slices (e.g. lightcones)

• Observe with chosen instrument (easiest step)

Virtual Observations – Star light

• Population synthesis– Save initial mass, age, and composition of

individual star particles– Assume IMF

• Dust column– Save mass and composition of individual

gas particles– Assume reddening law– Require fast calculation of SPH column

densities

Virtual Observations – Gas absorption

• Require gas mass, position, velocity, density, temperature, composition

• Assume ionization equilibrium and use ionization balance tables (e.g. CLOUDY)– Collisional ionization temperature– Photo-ionization density,

temperature, radiation field

Virtual Observations – Gas emission

• Require gas mass, position, density, temperature, composition, (velocity)

• Assume ionization equilibrium and use emissivity tables (e.g. CLOUDY)

• Dust columns

On-demand Virtual Observations of cosmological simulations

• Computed from physical properties (e.g. gas density, temperature)

• Computed from pre-calculated “ideal” observables

– Example: narrow band gas emission for various assumed radiation fields, chemical compositions, or density/temperature cuts– Allows one to vary both physical assumptions and instrumental characteristics– Computationally expensive

– Example: narrow band gas emission for a fixed radiation field and chemical composition– Cannot vary physical assumptions– Relatively cheap

VOs from physical properties • Challenge: Analysis of 3-D simulations is

typically memory-intensive (512^3 1 single precision array is 0.5 GB)

• Solution: Prepare data with reduced

dimensionality, calculate on demand–1-D: Absorption spectra, dust columns–2-D: Images from integrated quantities

Problem: Emission & (gas) absorption depends on local 3-D rather than projected 2-D properties

On-demand VOs

• Feasible now or in the near future:– (VOs based on) object catalogues– Absorption spectra– SZ effect– Images of stellar light (though maybe

not full lightcones)– Other VOs from pre-calculated

observables

Discussion points:1. Are raw cosmological simulation data

sets too large to put in the VO? 2. Is there a place for non-VO, processed

data products in the VO? (e.g. physical properties)

3. Must VO software for non-simulators be like a black box?

4. Is it desirable to create black boxes for non-simulators?

5. How to prevent wasting of resources by wrong use of VO software?