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Using Graphics Processing Units as Accelerators for Pulsar Dedispersion

By : Arjun Radhakrishnan Supervisor : Prof. M. Inggs. Using Graphics Processing Units as Accelerators for Pulsar Dedispersion. Outline. Pulsars and pulsar dispersion Graphics Processing Units (GPUs) Research method and Results Conclusion and Future Work. 2. Pulsars & Dispersion.

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Using Graphics Processing Units as Accelerators for Pulsar Dedispersion

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  1. By : Arjun Radhakrishnan Supervisor : Prof. M. Inggs Using Graphics Processing Units as Accelerators for Pulsar Dedispersion

  2. Outline • Pulsars and pulsar dispersion • Graphics Processing Units (GPUs) • Research method and Results • Conclusion and Future Work 2

  3. Pulsars & Dispersion • Pulsars are highly magnetised rotating neutron stars • They emit beams of electromagnetic radiation from their poles Figure 1: A Pulsar with its ‘lighthouse’ beam [hartrao.ac.za] 3

  4. Dispersion • Pulsar emissions are distorted upon passing through the ionised Interstellar Medium (ISM) • Lower frequency components of the pulse are delayed more than higher frequencies

  5. Dedispersion 5 Figure 2: Dedispersion2

  6. Graphics Processing Units • Class of consumer parallel processor that has come into use in the last 15 years • Use growing exponentially due to demand from billion-dollar video game industry • NVIDIA and AMD (ATI) are currently major players in the industry • GPUs do not have much on-chip memory – can pack in lots of compute power 6

  7. GPUs for Pulsar Dedispersion • Justification for SKA • Large frequency range • 1TB of data per minute • SKA needs real-time processing as data storage is not feasible • No communication needed between GPU kernels 7

  8. Method • Worked at UIUC on the QP GPU cluster • Implemented the following coherent pulsar dedispersion algorithm4: • Fourier transform input signal • Apply a phase rotation • Inverse Fourier transform 8

  9. Results • Code testing is still being conducted • Some trends noted are: • Speedup of up to 5x over CPU implementation • Performance improved approximately linearly with the number of GPUs used • Best performance for larger datasets (minimises effect of IO bottleneck) 9

  10. Conclusion and Future Work • GPUs definitely show promise in this application • Further speedup may be possible by using an asynchronous data transfer • Analyse the network requirements and limitations when deployed 10

  11. References • Cordes & McLaughlin (2003), “Searches for Fast Radio Transients”, The Astronomical Journal, vol. 596, pp. 1142-1154 • Jim Cordes, “The SKA as a Radio Synoptic Survey Telescope: Widefield Surveys for Transients, Pulsars and ETI”, SKA Memo 97 • NVIDIA, NVIDIA CUDA Programming Guide • Walter Brisken, “Real-time Digital Signal Processing for Radio Astronomy” AstroGPU 11

  12. Thank You Questions? 12

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