Roger Blandford
description
Transcript of Roger Blandford
Kavli IPMU 19 v 2012
Computational Astrophysicsat the
Kavli Institute for
Particle Astrophysics and Cosmology
at Stanford
Roger Blandford
Kavli IPMU 2
High Performance Computing @ KIPAC
• Truism that steadily increasing computational power has transformed science in general and astrophysics in particular
• High performance computing contributes to:– Simulation of complex physics under current paradigm– Optimization of telescope design– Exploration of model space– Data management, analysis, archiving and mining– Explanation of discoveries – Public dissemination of results
• Recent example of each type of computing
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Simulation of Complex Physics under Current
Paradigm• Dark matter clumping in expanding
universe• Crucial for understanding:
– Missing dwarfs problem– Direct detection of WIMPs– Indirect detection of g-rays
• Abel, Hahn, Kaehler have implemented a new approach to dark matter simulations following trajectories in 6D phase space
• Testing and comparison with 3D results
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Warm Dark Matter Simulation
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Optimizing Telescope Design • Telescopes are typically designed for
both specific goals and discoveries• e.g. LSST (2014 start?; 2020
operate?)– Dark energy through weak lensing– Light from distant star
• Deflected by intervening gravitational field• Distorted by atmosphere• Reflected by moving mirrors, refracted by thick lenses• Detected and counted by noisy CCD • Analyzed using new algorithms
– Peterson, Chang, Bard… are building simulator 9 v 2012
=>w(a)
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LSST Simulation
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Exploration of Model Space• Complex physical processes have to
be modeled phenomenologically to tease out empirical rules– e.g. how do we associate luminous galaxies with dark
matter and gas distribution• Busha,Wechsler, Kaehler adapt
Bolshoi simulation and compare with Sloan survey– Visually indistinguishable – Compare measurable correlation functions
• Understand rules in terms of basic physics
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Bolshoi-SDSS Comparison
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Data Management, Analysis, Archiving and
Mining • Telescopes produce data challenges• e.g. Dubois manages Fermi data
pipeline– Event processing in 15 min– Alerts, triggers– 1600 CPUs, 4PB disk, tapes– Back up on campus; 1200 CPU system in Lyon
• LSST – 20 TB per night=>60 PB raw data, 15 PB for catalog– =>300PB data volume; >150 Tflops9 v 2012
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GN
HEASARC
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DELTA7920H •
White Sands
TDRSS SNS & Ku
LAT Instrument Science Operations
Center (SLAC)
GBM Instrument Operations Center
GRB Coordinates Network
• Telemetry 1 kbps•
-•
S
Alerts
Data, Command Loads
Schedules
Schedules
Mission Operations Center (MOC)
Fermi Science Support Center
• msec•
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•
Fermi Spacecraft
Large Area Telescope& GBMGPS
Fermi MISSION ELEMENTS
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Explanation of Discoveries• Unexpected is expected in
astronomy• Many astrophysical phenomena have
no credible (or many incredible) explanations
• e.g. X-ray quasi-periodic oscillations in stellar black hole systems ~ 300 Hz, 3:2?
• McKinney, Tchekhovskoy, RB simulated accretion onto black hole with strong field– 3D RMHD, >106m, geometries initial conditions– Efficient, quasi-stable jets, extract spin energy– Outflows, winds, Jet-Disk Oscillation– Relativistic radiative transfer underway
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Public Dissemination of Results
• Education and Public Outreach is important part of KIPAC mission
• Staff, postdocs and students regularly present shows, lead tours, visit schools…
• Pierre Schwob Computing and Information Center hosts 3D theater and Hyperwall
• Analysis AND outreach• New graphics, rendering tools,
hardware– GPUs, suitcase system
•
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Third Grade in 3D
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Summary• Truism that steadily increasing
computational power has transformed science in general and astrophysics in particular
• High performance computing contributes to:– Simulation of complex physics under current paradigm– Optimization of telescope design– Exploration of model space– Data management, analysis, archiving and mining– Explanation of discoveries – Public dissemination of results
• Increasingly, these functions are combined in strongly coupled activities
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Congratulations
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Reionization (Alvarez et al)
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Dark matter streams (Hahn et al)
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Large scale structure (Abel et al)
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Clusters (Wu et al)
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Hyperwall (Adesanya…)
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