massive generation of mock catalogues with...

cosmocomp workshop, Tireste September 2012

Massive generation of mock catalogues with Pinocchio

A progress report P. Monaco, E. Sefusatti, M. Calcioni, S. Borgani, R. Sheth(Univ. Trieste & ICTP)

P. Monaco, E. Sefusatti, S. Borgani, R. Sheth, A. Paranjape, T. Theuns


Need for samples of >1000 of simulated catalogues

● Simulating galaxies in past light cones over volumes » 1Gpc3

● Need » 20003 particles per realization

● Need » 1000 realizations to compute covariance matrices

● Disc space may be an issue more than CPU time!


Computers

● Moving to the Blue Gene tech cycle: massively parallel computers with limited resources per core and limited i/o.

● An optimal code should have very good scaling (>1000 tasks) and output only the final results

Fermi@CINECA


PINpointing Orbit Crossing-Collapsed HIerarchical Objects

He's cheating:he's not N-body,he's way too fast!

http://adlibitum.oats.inaf.it/monaco/pinocchio/

P.M., Tom Theuns, Giuliano Taffoni, Fabio Governato, Tom Quinn & Joachim Stadel, 2002, ApJ, 564, 8

P.M.,, Tom Theuns & Giuliano Taffoni, 2002, MNRAS, 331, 587

Giuliano Taffoni, P.M. & Tom Theuns, 2002, MNRAS, 333, 623


● Uses Lagrangian Perturbation Theory and the excursion sets approach to predict the hierarchical formation of DM halos

● To take full account of correlation of trajectories, it works on realizations of a linear density field in a rectangular grid (like N-body simulations)

The algorithm


STEP 1:● Generate a linear density field on a grid● Gaussian-smooth it on ~20 smoothing radii to

generate one trajectory per grid point● For each smoothing and for each grid point compute

the collapse time with the “ellipsoidal truncation” of 3LPT (P.M. 1997)

● Compute the expected collapse time of the “particle” as the first upcrossing of the trajectory and store the velocity field at the same smoothing radius

● It requires 9 FFTs per smoothing

The algorithm


STEP 1.5:● Redistribute grid points from planes (FFT) to

sub-volumes with a “safety” boundary of ~30 Mpc

The algorithm


The algorithm: step 2

● scroll the particle list in increasing order of collapse time● peaks of the (inverse of the) collapse time are seeds of new

halos● if a collapsing particle “touches” a halo then

– displace particle and halo with Zel'dovich approx. to check whether they get “near enough” in the Eulerian space

● if they do, then accrete the particle on the halo● if they do not, then tag the particle as “filament”

● if a collapsing particle touches more than a halo then

– check whether the particle is accreted onto a halo– displace halos to check whether they get “near enough”

● if they do, merge them


peak -> new halo

1 neighbour: accretion

2 neighbours: accretion + merging

.


The mass function


The 2-point correlation function


The progenitor mass function


Filaments


Cleanly reconstructed halos


Object-by-object agreement


Accuracy of reconstruction


Latest developments

✔ No swapping of memory on disc to minimize i/o➔ RAM requirement: 110 bytes per grid point

✔ distribution of sub-boxes on tasks that works for big volumes

✔ Embedded N-GenIC to generate ICs● 2LPT and 3LPT displacements● built-in output in the past-light cone


Scaling test 17203 grid of size 720 Mpc/h run on PLX@cineca


grid size scaled with the number of processors

Scaling test 2


An example

● Millennium-like simulation● 500 Mpc/h box with 21603 grid points● run on 360 cores on PLX@CINECA (linux

cluster)● writing of 6 full halo catalogues (merger

histories are build with very high time sampling)● CPU time: 214.5 hours● Elapsed time: 35min 45s


Preliminary: the mass function

z=0

z=1z=2


Preliminary: power spectrum

Las Damas: “Oriana” simulation


Preliminary: power spectrum

massive generation of mock catalogues with...

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