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Spotting Radio Transients With the help of GPUs

Ben Barsdell Matthew Bailes Christopher Fluke David Barnes. Spotting Radio Transients With the help of GPUs. Background. Goal is to discover new pulsars and radio transients. Survey specs 400 MHz BW @ 1381.8 MHz 1024 freq. channels 64 μ s time resolution 2-bit sampling.

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Spotting Radio Transients With the help of GPUs

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  1. Ben Barsdell Matthew Bailes Christopher Fluke David Barnes Spotting Radio TransientsWith the help of GPUs

  2. Background • Goal is to discover new pulsars and radio transients Survey specs 400 MHz BW @ 1381.8 MHz 1024 freq. channels 64μs time resolution 2-bit sampling High Time Resolution Universe (HTRU) survey Uses the Parkes 64m radio telescope

  3. Multi-beam receiver Event Follow-up observation Parkes Swinburne Dedispersed time series Masked data Filterbank data Freq. Freq. DM RFI removal Time Time Time Incoherent dedispersion List of candidates Signal search Ben Barsdell - ADASS 2011

  4. The Plan • Current pipeline takes> 30 mins per 10 min observation • Necessitates off-line processing • Means transfers, tapes and long waits • Would like to speed things up to real-time • Instant feedback and follow-up observations • Triggered baseband data dumps • How to do it? • Time to bring out the heavy artillery… Ben Barsdell - ADASS 2011

  5. The GPU Ben Barsdell - ADASS 2011

  6. Telescope receiver beams Event Follow-up observation Parkes Swinburne Masked data Dedispersed time series Filterbank data Freq. Freq. DM RFI removal Time Time Time Incoherent dedispersion List of candidates Signal search Ben Barsdell - ADASS 2011

  7. RFI mitigation The detection pipeline Incoherent dedispersion Baseline removal Fourier search Single pulse search Fourier transform Matched filter Harmonic summing RFI mitigation Sigma clip Report candidates Ben Barsdell - ADASS 2011

  8. RFI mitigation www.clker.com • Interference is a big problem • No easy solution • Military radar too important • Prime-time TV too popular • Some things can be done • Sigma clipping • Spectral kurtosis • Coincidence rejection Ben Barsdell - ADASS 2011

  9. Coincidence rejection • Use multi-beam receiver as reference antennas • Assume RFI is not localised • Apply simple coincidence criteria: • E.g., 3σ in 4+ beams => RFI • Or use Eigen-decomposition approach • Run on GPU as a straightforward transform • RFI_mask[i] = is_RFI(multibeam_data[i]) • Note: Eigen-decomp method makes is_RFI()trickier Ben Barsdell - ADASS 2011

  10. Dedispersion ISM Broadband signal Dispersed signal Radio source e- Frequency (MHz) Frequency (MHz) ? Phase Phase Ben Barsdell - ADASS 2011 • Unknown distance => search through DM space • Pick DM, dedisperse, search, repeat • ~1200 DM trials

  11. Dedispersion • Computationally intensive problem • Biggest time-consumer • Runs really well on a GPU • Lots of parallelism • High arithmetic intensity • Good memory access patterns • No branching Ben Barsdell - ADASS 2011

  12. Other algorithms • Baseline removal • Subtract running mean • Port to GPU using parallel prefix sum • Matched filtering • Convolve with 1D boxcar • Can also use parallel prefix sum • Sigma cut + peak find • Threshold and segment • Port to GPU using segmented reduction Ben Barsdell - ADASS 2011

  13. GPU dedispersion Ben Barsdell - ADASS 2011

  14. Preliminary results • Dedispersion: 20 mins  < 2.5 mins • Using ‘direct’ method on 1 Tesla C2050 GPU • Time-binning gives further 2x speed-up • Details in Barsdell et al. 2012 • Other algorithms mostly complete • 1 beam / GPU should be easy • 2 beams / GPU within reach? Ben Barsdell - ADASS 2011

  15. Software/hardware configuration Process Operation 1 Recv beam Dedisperse Send DMs Recv beams Continue… . . . . . . . . . 2 Send DMs Recv beams . . . 3 Recv beams 4 Recv beams . . . Ben Barsdell - ADASS 2011

  16. Deployment Destined for Parkes Real-time results RFI ‘weather report’ Ben Barsdell - ADASS 2011

  17. Looking ahead • Real-time radio transient detection promises to • Simplify the data processing procedure • Enable immediate follow-up observations • Allow capture of high-resolution baseband data for significant events • Catch things like the ‘Lorimer burst’ as they happen! Ben Barsdell - ADASS 2011

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