  Destriping is a well-established technique for removing low-frequency
correlated noise from Cosmic Microwave Background (CMB) survey data. In this
paper we present a destriping algorithm tailored to data from a polarimeter,
i.e. an instrument where each channel independently measures the polarization
of the input signal.
  We also describe a fully parallel implementation in Python released as Free
Software and analyze its results and performance on simulated datasets, both
the design case of signal and correlated noise, and with additional systematic
effects.
  Finally we apply the algorithm to 30 days of 37.5 GHz polarized microwave
data gathered from the B-Machine experiment, developed at UCSB. The B-Machine
data and destriped maps are made publicly available.
  The purpose is the development of a scalable software tool to be applied to
the upcoming 12 months of temperature and polarization data from LATTE (Low
frequency All sky TemperaTure Experiment) at 8 GHz and to even larger datasets.
