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Computational Cosmology

This collaboration meeting discusses the need for simulations in cosmology to extract valuable scientific insights from surveys. Topics covered include dark matter, dark energy, neutrino mass, inflation, gravitational instability, hydrodynamics, and the impact of radiation fields on structure. The meeting also highlights recent developments in medium-scale computing and the Computational Cosmology Initiative.

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Computational Cosmology

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  1. Computational Cosmology Argonne-Fermilab-University of Chicago Collaboration Meeting Scott Dodelson

  2. NeedSimulationsto ExtractScience from Surveys • Dark Matter • Dark Energy • Neutrino Mass • Inflation • Gravitational Instability is nonlinear • Baryons governed by hydrodynamics • Radiation Field affects and is affected by structure • Stars form, Supernovae explode, … Scott Dodelson

  3. Progress 1000 Core Cluster at New Muon Lab ANL LDRD KICP, TAG Investments DOE Dark Energy Grant FRA Grant Task Force Report 560 Core Cluster at New Muon Lab DOE Lab Meeting Presentation to PAC Scott Dodelson

  4. Need for Medium Scale Computing • Cosmological surveys often require many medium-sized simulations that can be run only on a dedicated cluster • Medium scale local clusters are ideally matched to the problems at hand • Medium scale clusters allow further development to scale to the largest machines available • Medium scale machines needed for analysis Scott Dodelson

  5. Recent Developments November, 2008: SLAC, LBL, FNAL approached by DOE-HEP to submit White Paper for medium scale Computational Cosmology Initiative December, 2008: “Scientific Challenges of Computing at Extreme Scale” Workshop at SLAC. Highest ranked Grand Challenge in astrophysics: Cosmic structure formation probes of the dark universe Today: Close to final draft of medium scale White Paper to be submitted in February, 2009. Prelude to proposal Scott Dodelson

  6. Computational Cosmology Initiative • The Cosmology Data Grid: A user facility, wide area data repository of numerical simulations using standardized data formats. It will be the main avenue to share and disseminate simulation data within and outside the collaborations (DES, LSST, JDEM). • Diverse computing hardware ranging from shared memory architectures to machines optimized for collaboration codes. Scott Dodelson

  7. Continued Collaboration with UC, ANL • KICP members have access to FNAL cluster; Kravtsov collaboration member • KICP planning for NSF Center Renewal: Discussions revolving around role of computing • ANL partner in DES • ANL host to Blue Gene. Natural partner in quest to scale codes to high capability machines Scott Dodelson

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