1. by Arjun Radhakrishnan supervised by Prof. Michael Inggs
Accelerating Coherent Pulsar De-dispersion on Graphics Processing Units by
Arjun Radhakrishnan
supervised by
Prof. Michael Inggs

2. Outline Graphics Processing Units (GPUs) Pulsars Pulsar De-dispersion
Motivation
Implementation
Results
Conclusion & Future Work

3. Graphics Processing Units
GPUs are massively parallel processors that are present on consumer graphics cards
Generally used to render 3D objects on screen and calculate the colour of pixel to display
*Source: [7]
Are mass market products due to the video game industry
Performance tracks Moore's Law since the majority of on-chip space is devoted to compute units as opposed to cache on CPUs

4. Why Use GPUs?
Figure 1: Peak floating point performance of NVIDIA GPUs vs Intel CPUs [2]

5. Figure 2: Pulsar Model [3]
Pulsars
Highly magnetised, rapidly rotating neutron stars formed after a supernova
Pulsars emit beams of electromagnetic radiation from their magnetic poles
Beams sweep in a circular path called the “lighthouse effect”
Produce periodic pulses when the pulse sweeps Earth
Figure 2: Pulsar Model [3]

6. Pulsar 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

7. Figure 3: Pulsar De-dispersion [4]
Pulsar emissions are distorted upon passing through the ionised Interstellar Medium (ISM)
Lower frequency components of the pulse are delayed more than higher frequencies
Correct for the dispersion by shifting the received signal a certain amount
Figure 3: Pulsar De-dispersion [4]

8. Coherent De-dispersion
Coherent de-dispersion is the most accurate method of removing the dispersion effects of the Interstellar Matter
Preserves amplitude and phase information from the receiving signal
Convolve the voltage signal with the inverse transfer function of the ISM
This transfer function is a function of the Dispersion Measure (DM) of the signal got from models of the galactic electron density
In practice we use the Fast Fourier Transform (FFT) to make the convolution operation a multiplication in the frequency domain and then apply an inverse FFT

9. Motivation Why study Pulsars? GPU acceleration for MeerKAT
A major SKA Science driver: Detection of gravitational waves and tests of strong field relativity; Analysing black holes
GPU acceleration for MeerKAT
Large frequency range (Low: 0.5 – 2.5 GHz, High: 8 – 14.5GHz)
High bandwidth per polarisation (4GHz final)
Large number of channels (16384)
>10GB of data per second
Even more important for SKA since precision will be a high priority and data storage is not feasible

10. Implementation Considerations
Both CPU and GPU were tested with single-precision floating point
A bottleneck for GPU computing is the time taken to send data to it from main memory – minimise as much as possible
Use asynchronous data transfers to hide the latency
Re-calculate rather than copy data across
Use shared memory on the GPU for calculations and store to global memory at the end
Source data file used is fake dual polarisation data generated with a DM of 50pc/cm3 and 100MHz bandwidth centred on 1450MHz

11. Receive de-dispersed signal
Basic Program Flow
HOST
DEVICE
Read in Data
Copy to GPU memory
Allocate memory on GPU
Initiate GPU Kernel
Begin De-dispersion
Parallel FFT
Parallel FFT
...
Parallel FFT
V(f0) . H-1(f0)
V(f1) . H-1(f1)
...
V(fn) . H-1(fn)
Inverse FFT
Inverse FFT
...
Inverse FFT + +
Output Array
Receive de-dispersed signal
Send Data Back to Host
Free Memory
Figure 4: Program flow

12. Figure 5: Left: Overall speedup (5x) Right: Kernel Speedup (12x)
Results
Figure 5: Left: Overall speedup (5x) Right: Kernel Speedup (12x)

13. Results Was able to coherently de-disperse 50MHz on 1 GPU
Used 2 GPUs for the full 100MHz
Scaling across multiple GPUs was linear
Using larger transfer functions was found to increase performance since there was less of an overhead in memory access times

14. Conclusion GPUs are significantly faster than CPUs for de-dispersion
Enabled real-time coherent de-dispersion for the dataset used
Coherent de-dispersion of a 100MHz bandwidth signal requires multiple GPUs at present
Faster memory access would greatly improve overall speedup
Currently testing with real undetected pulsar data

15. Thank You!
Questions?

16. References
D. R. Lorimer and M. Kramer, Handbook of Pulsar Astronomy Cambridge University Press, 2005
NVIDIA CUDA Programming Guide
D. Manchester, “CSIRO ATNF Pulsar Education Page”
Jim Cordes, “The SKA as a Radio Synoptic Survey Telescope: Widefield Surveys for Transients, Pulsars and ETI”, SKA Memo 97
John Rowe Animation/Australia Telescope National Facility, CSIRO [Online].
Cornell University Dept. of Astronomy, “Legacy Pulsars: Homepage” [Online].
VR-Zone, “The NVIDIA GeForce GTX 280 1GB bare,” [Online]. zone.com/articles/nvidia-geforce-gtx-280-preview/5872.html?doc=5872