Cosmology with Galaxy Clusters: Observations meet High-Performance-Computing
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1 Cosmology with Galaxy Clusters: Observations meet High-Performance-Computing Julian Merten (ITA/ZAH) Clusters of galaxies GPU lensing codes Abell 2744 CLASH: A HST/MCT programme
2 Clusters of galaxies DM dominated M, Mpc scale Baryonic component not dominant, though not negligible Observable in 3 regimes Cosmic laboratories Strong grav. lenses Still pose riddles
3 Cluster puzzles
4 Cluster puzzles Ludlow11+
5 Cluster puzzles Ludlow11+ Zitrin10+
6 Cluster puzzles Ludlow11+ Zitrin10+ e.g. Broadhurst08+
7 Cluster puzzles Ludlow11+ Zitrin10+ e.g. Broadhurst08+ e.g. Lee10+
8 Joint lensing reconstructions Fully nonparametric approach χ 2 (ψ) =χ 2 w(ψ)+χ 2 s (ψ)+... Adaptive-grid method Does not assume light traces mass Easily extendable Future: Kinematics, gas constraints
9 Simulating lensing
10 Simulating lensing Meneghetti, Rasia, JM et al. 2010
11 Modern implementation: GPUs Respect SIMT: hundreds/thousands of threads Respect memory hierarchy Almost no need for synchronisation Balance of parallelisation and work per thread Use CUDA C or OpenCL (Some alternatives upcoming) Available libraries: CUBLAS, CUFFT, CUSPARSE, CURAND, CULA Combination with MPI possible Some child deseases
12 Modern implementation: GPUs Respect SIMT: hundreds/thousands of threads Respect memory hierarchy Almost no need for synchronisation Balance of parallelisation and work per thread Use CUDA C or OpenCL (Some alternatives upcoming) Available libraries: CUBLAS, CUFFT, CUSPARSE, CURAND, CULA Combination with MPI possible Some child deseases
13 GPU systems and speed-up NVIDIA TESLA C2050 (Fermi) 1 TFLOP peak (single) 500 GFLOP (double) 3 GB ECC global memory 14 (16) Multi-processors 448 (512) streaming cores Speed-up Our current workhorse: Jabba 2x quadcore 2.4 Ghz 16 GB ECC main memory NVIDIA TESLA C1060 NVIDIA TESLA C2050 Cluster performance for 5000 EUR B lk = a i b j C ij D ik E jl one core CPU: 82.3 s 240 core GPU: 1.03 s + ITA/ZITI cluster Kolob + Granted SFB machine
14 One of the pearls: Abell 2744 Owers10+
15 One of the pearls: Abell band HST/ACS imaging, PI: R. Dupke
16 One of the pearls: Abell 2744 Cypriano 04+
17 One of the pearls: Abell 2744 JM11+ in prep.
18 Creation of cosmic structure JM11+ in prep. (next week) D. Coe (STScI) R.Dupke (Michigan) R. Massey (Edinburgh) A. Zitrin (Tel Aviv) T. Broadhurst (Bilbao) E. Cypriano (S. Paolo) N. Okabe (Taipei) M. Meneghetti (Bologna) J. Rhodes (JPL) L. Moustakas (JPL)
19 CLASH: A HST/MCT programme Science Drivers To map dark matter in galaxy clusters To detect SN out to redshifts z > 1.5 To detect and characterise z > 7 galaxies To study the galaxies in and behind the clusters
20 CLASH: A HST/MCT programme To map dark matter in galaxy clusters To detect SN out to redshifts z > 1.5 To detect and characterise z > 7 galaxies Science Drivers To study the galaxies in and behind the clusters Granada, September 20, 2010
21 The observations: 524 HST orbits 25 clusters (0.18 < z < 0.9) X-ray selected (Chandra archival data) relaxed ACS/WFC3 in parallel 16 bands & 20 orbits per cluster (NIR - NUV)
22 The observations: 524 HST orbits 25 clusters (0.18 < z < 0.9) X-ray selected (Chandra archival data) relaxed ACS/WFC3 in parallel 16 bands & 20 orbits per cluster (NIR - NUV) SUBARU BVRIZ weak lensing
23 The observations: 524 HST orbits 25 clusters (0.18 < z < 0.9) X-ray selected (Chandra archival data) relaxed ACS/WFC3 in parallel 16 bands & 20 orbits per cluster (NIR - NUV) SUBARU BVRIZ weak lensing GTC/VLT/Magellan spectroscopy
24 The observations: 524 HST orbits 25 clusters (0.18 < z < 0.9) X-ray selected (Chandra archival data) relaxed ACS/WFC3 in parallel 16 bands & 20 orbits per cluster (NIR - NUV) SUBARU BVRIZ weak lensing H EIDELBERG GTC/VLT/Magellan spectroscopy Bolocam/AMiBA/SZA/Mustang SZE observations
25 First results: Abell 383 First SL mass models, Zitrin11+ in prep. Colour composite using Trilogy, credit Dan Coe
26 First results: Abell 383 WL contours on ACS (JM) WL contours on SUBARU (JM)
27 Conclusions Galaxy clusters are an exciting topic for cosmology Combined lensing codes are able to reconstruct their mass distribution to a few percent For testing and comparing to simulations, the full pipeline has to be simulated GPUs are an energy -and price-efficient way to implement time-consuming codes Abell 2744: cosmic structure formation at work CLASH will help answering some open questions, at least we hope so
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