  With ever increasing data rates produced by modern radio telescopes like
LOFAR and future telescopes like the SKA, many data processing steps are
overwhelmed by the amount of data that needs to be handled using limited
compute resources. Calibration is one such operation that dominates the overall
data processing computational cost, nonetheless, it is an essential operation
to reach many science goals. Calibration algorithms do exist that scale well
with the number of stations of an array and the number of directions being
calibrated. However, the remaining bottleneck is the raw data volume, which
scales with the number of baselines, and which is proportional to the square of
the number of stations. We propose a 'stochastic' calibration strategy where we
only read in a mini-batch of data for obtaining calibration solutions, as
opposed to reading the full batch of data being calibrated. Nonetheless, we
obtain solutions that are valid for the full batch of data. Normally, data need
to be averaged before calibration is performed to accommodate the data in
size-limited compute memory. Stochastic calibration overcomes the need for data
averaging before any calibration can be performed, and offers many advantages
including: enabling the mitigation of faint radio frequency interference;
better removal of strong celestial sources from the data; and better detection
and spatial localization of fast radio transients.
