Single-pulse Detection Algorithms for Real-time Fast Radio Burst Searches Using GPUs

The detection of non-repeating or irregular events in time-domain radio astronomy has gained importance over the last decade due to the discovery of fast radio bursts. Existing or upcoming radio telescopes are gathering more and more data, and consequently, the software, which is an important part of these telescopes, must process large data volumes at high data rates. Data has to be searched through to detect new and interesting events, often in real time. These requirements necessitate new and fast algorithms that must process data quickly and accurately. In this work, we present new algorithms for single-pulse detection using boxcar filters. We have quantified the signal loss introduced by single-pulse-detection algorithms, which use boxcar filters, and based on these results, we have designed two distinct "lossy" algorithms. Our lossy algorithms use an incomplete set of boxcar filters to accelerate detection at the expense of a small reduction in detected signal power. We present formulae for signal loss, descriptions of our algorithms and their parallel implementation on NVIDIA GPUs using CUDA. We also present tests of correctness, tests on artificial data, and the performance achieved. Our implementation can process SKA-MID-like data 266x faster than real time on an NVIDIA P100 GPU and 500x faster than real time on an NVIDIA Titan V GPU with a mean signal power loss of 7%. We conclude with prospects for single-pulse detection for beyond the SKA era, nanosecond time-resolution radio astronomy.