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Square Kilometre Array Computational Challenges Paul Alexander

Square Kilometre Array Computational Challenges Paul Alexander. What is the Square Kilometre Array (SKA). Next Generation radio telescope – compared to best current instruments it is ... ~100 times sensitivity ~ 10 6 times faster imaging the sky

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Square Kilometre Array Computational Challenges Paul Alexander

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  1. Square Kilometre ArrayComputational ChallengesPaul Alexander

  2. What is the Square Kilometre Array (SKA) • Next Generation radio telescope – compared to best current instruments it is ... • ~100 times sensitivity • ~ 106 times faster imagingthe sky • More than 5 square km ofcollecting area on sizes3000km E-MERLIN eVLA 27 27m dishes Longest baseline 30km GMRT 30 45m dishes Longest baseline 35 km

  3. What is the Square Kilometre Array (SKA) • Next Generation radio telescope – compared to best current instruments it is ... • ~100 times sensitivity • ~ 106 times faster imaging the sky • More than 5 square km of collecting area on sizes3000km • Will address some of the key problems of astrophysics and cosmology (and physics) • Builds on techniques developed in Cambridge • It is an interferometer • Uses innovative technologies... • Major ICT project • Need performance at low unit cost

  4. Dishes

  5. Phased Aperture array

  6. also a Continental sized Radio Telescope • Need a radio-quiet site • Very low population density • Large amount of space • Possible sites (decision 2012) • Western Australia • Karoo Desert RSA

  7. Sensitivity comparison SKA2 SKA1 EVLA LOFAR

  8. SKA2 ~ 250 Dense Aperture Array Stations 300-1400MHz ~ 2700 Dishes 3-Core Central Region Wide Band Single Pixel Feeds ~250 Sparse Aperture Array Stations 70-450MHz Phased Array Feeds800 MHz – 2GHz Artist renditions from Swinburne Astronomy Productions

  9. SKA1 ~ 300 Dishes 2-Core Central Region Wide Band Single Pixel Feeds ~50 Sparse Aperture Array Stations 70-450MHz Artist renditions from Swinburne Astronomy Productions

  10. SKA Timeline • 2019Operations SKA1 2024: Operations SKA2 • 2019-2023 Construction of Full SKA, SKA2€1.5B • 2016-201910% SKA construction, SKA1€300M • 2012 Site selection • 2012 - 2016Pre-Construction: 1 yr Detailed design€90M • PEP 3 yrProduction Readiness • 2008 - 2012System design and refinement of specification • 2000 - 2007Initial concepts stage • 1995 - 2000Preliminary ideas and R&D

  11. Work Packages in the PEP Management System Science Maintenance and support /Operations Plan Site preparation Dishes Aperture arrays Signal transport & networks Signal processing Science data processor Telescope manager Power SPO Work Package Contractors

  12. Work Packages in the PEP Management System Science Maintenance and support /Operations Plan Site preparation Dishes Aperture arrays Signal transport & networks Signal processing Science data processor Telescope manager Power SPO Work Package Contractors UK (lead), AU (CSIRO…), NL (ASTRON…) South Africa SKA, Industry (Intel, IBM…)

  13. Very Brief Introduction to Radio Astronomy Imaging

  14. Standard interferometer s Astronomical signal (EM wave) • Visibility: • V(B) = E1 E2* • = I(s) exp( iwB.s/c ) • Resolution determined by maximum baseline • qmax ~ l / Bmax • Field of View (FoV) determined by the size of each dish • qdish ~ l / D B . s Detect & amplify B 1 2 Digitise & delay Correlate X X X X X X Integrate  visibilities SKY Image Process Calibrate, grid, FFT

  15. Aperture arrays Digitise, delay & beam form • Beam form: • Apply delay gradients to point electrically • Multiple delay gradients • many beams and large FoV Correlate X X X X X X SKY Image Process Calibrate, grid, FFT

  16. Aperture arrays • Aperture-Array station • ~25000 phased elements • Equivalent to one dish • These are then cross-correlated Digitise, delay & beam form • Beam form: • Apply delay gradients to point electrically • Multiple delay gradients • many beams and large FoV Correlate X X X X X X SKY Image Process Calibrate, grid, FFT

  17. Formulation • What we measure from a pair of telescopes: • In practice we have to deal with this equation, butfor simplicity consider a scalar model m l • The delays allow us to follow a point on the sky • The Ji are direction dependent Jones matrices which include the effects of: • propagation from the sky through the atmosphere • scattering • coupling to the antenna/detector • gain s

  18. Formulation • Where we include time delays to follow a central point. In terms of direction cosines relative to the point we follow and for a b = (u,v,w) • If we can calibrate our system we can apply our telescope-dependent calibrations and then for small FoV we can approximate • And the measured data are just samples of this function • In this case we can estimate the sky viaFourier inversion and deconvolution m l s

  19. Formulation • Sampling of the Fourier plane is determined by the positioning of the antennas and improved by the rotation of the earth m l We s

  20. The Science Aims and the Imaging Challenges

  21. SKA Key Science Drivers ORIGINS • Neutral hydrogen in the universe from the Epoch of Re-ionisationto now • When did the first stars and galaxies form? • How did galaxies evolve?Role of Active Galactic Nuclei • Dark Energy, Dark Matter • Cradle of Life FUNDAMENTAL FORCES • Pulsars, General Relativity & gravitational waves • Origin & evolution of cosmic magnetism TRANSIENTS (NEW PHENOMENA) Science with the Square Kilometre Array (2004, eds. C. Carilli & S. Rawlings, New Astron. Rev., 48)

  22. Galaxy Evolution back to z~10? HDF VLA ~ 3000 galaxies ~15 radio sources

  23. Galaxy Evolution back to z~10? HDF SKA

  24. The Imaging Challenge • This illustrates one of our main challenges • To make effective use of the improved sensitivity we face an immediate problem • Typically within the field of view of the telescope the noise level will be ~106 – 107 times less than the peak brightness • We have to achieve sufficiently good calibration and image fidelity to routinely achieve a “dyanamic range” of > 107:1 • With very hard work now we can just get to 106:1 in some fields

  25. The Processing Challenge

  26. SKA2 wide area data flow 20 Gb/s 4Pb/s 16 Tb/s 24Tb/s 20 Gb/s

  27. SKA1 Data rates from receptors • Dishes • Depends on feeds, but illustrate by 2 GHz bandwidth at 8-bits • G = 64 Gb/s from each dish • For Phased Array feeds increased by number of beams (~20) • G ~ 1 Tb/s • For Low frequency Aperture Arrays : • Bandwidth is 380 MHz • Driven by the requirements of Field of View from the science requirements which from DRM is 5 sq-degrees  20 beams • G = 240 Gb/s • These are from each collector into the correlator or beam former • 300 dishes • 285 75-m AA stations • G(total) ~ 68 Tb/s

  28. Data Rates • After correlation the data rate is fixed by straightforward considerations • Must sample fast enough (limit on integration time) dt • Baseline  B/l • UV (Fourier) cell size  D/l • Must have small-enough channelwidth to avoid chromatic aberration maxB/l– B/(l+dl) maxWdt

  29. Data rates from the correlator Standard results for integration/dump time and channel width Data rate then given by # antennas # polarizations # beams word-length Can reduce this using baseline-dependent integration times and channel widths

  30. Example correlator data rates and products SKA1 • Aperture Array Line experiment (e.g. EoR) • 5sq degrees; 170000 channels over 250 MHz bandwidth • ~ 30 GB/s reducing quickly to ~ 1GB/s • Up to 500 TB UV (Fourier) data; Images (3D) ~ 1.5 TB • Continuum experiment with long baselines with the AA • 100 km baseline with the low-frequency AA • 1.2 TB/s reducing to ~ 12.5 GB/s • Up to 250 TB/day to archive if we archive raw UV data • Spectral-line imaging with dishes • Data rates ~ 50 GB/s; Images (3D) ~ 27 TB

  31. Example beam-formed data rates SKA1 • Pulsar search • Galactic-plane survey for pulsars • ~ 400 GB/s to de-disperser (hardware?) • Data products are of small volume as all analysis is done in pseudo real-time.

  32. Example Data rates SKA2

  33. Data Products • ~0.5 – 10 PB/day of image data • Source count ~106 sources per square degree • ~1010 sources in the accessible SKA sky, 104 numbers/record • ~1 PB for the catalogued data 100 Pbytes – 3 EBytes / year of fully processed data

  34. Processing model

  35. The SKA Processing Challenge Correlator Visibility processors Image formation Science analysis, user interface & archive switch AA: 250 x 16 Tb/s Dish: 3000 x 60 Gb/s ~ 200 Pflop to 2.5 Eflop ~10-100 PFlop ~ ? PFlop ~1 – 500 Tb/s Software complexity

  36. Conclusions • The next generation radio telescopes offer the possibility of transformational science, but at a cost • A major processing challenge • Need to analyse very large amounts of streaming data • Current algorithms iterative – need to buffer data • Problem too large to, for example, use a direct Bayesian approach • Are our (approximate) algorithms good enough to take into account all error effects that need to be modelled? • Only recently have we had to consider most of the effects – what have we forgotten? • Phased approach to SKA is very good for the processing – performance increasing and critically we can continually learn

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