Title: Towards exascale real-time RFI mitigation,
Abstract: We describe the design and implementation of an extremely scalable real-time
RFI mitigation method, based on the offline AOFlagger. All algorithms scale
linearly in the number of samples. We describe how we implemented the flagger
in the LOFAR real-time pipeline, on both CPUs and GPUs. Additionally, we
introduce a novel simple history-based flagger that helps reduce the impact of
our small window on the data.
By examining an observation of a known pulsar, we demonstrate that our
flagger can achieve much higher quality than a simple thresholder, even when
running in real time, on a distributed system. The flagger works on visibility
data, but also on raw voltages, and beam formed data. The algorithms are
scale-invariant, and work on microsecond to second time scales. We are
currently implementing a prototype for the time domain pipeline of the SKA
central signal processor.