  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 which 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 266$\times$ faster than real-time on a NVIDIA P100
GPU and 500x faster than real-time on a NVIDIA Titan V GPU with a mean signal
power loss of 7%. We conclude with prospects for single pulse detection for
beyond SKA era, nanosecond time resolution radio astronomy.
