  Atom probe tomography (APT) has matured to a versatile nanoanalytical
characterization tool with applications that range from materials science to
geology and possibly beyond. Already, well over 100 APT microscopes exist
worldwide. Information from the APT data requires a post-processing of the
reconstructed point cloud which is realized via basic implementations of data
science methods, mostly executed with proprietary software. Limitations of the
software have motivated the APT community to develop supplementary
post-processing tools to cope with increasing method complexity and higher
quality demands: examples are how to improve method transparency, how to
support batch processing capabilities, and how to document more completely the
methods and computational workflows to better align with the FAIR data
stewardship principles.
  One gap in the APT software tool landscape has been a collection of open
tools which support scientific computing hardware. Here, we introduce
PARAPROBE, an open source, efficient tool for the scientific computing of APT
data. We show how to process several computational geometry, spatial
statistics, and clustering tasks performantly for datasets as large as two
billion ions. Our parallelization efforts yield orders of magnitude performance
gains and deliver batch processing capabilities. We contribute these tools in
an effort to open up APT data mining and simplify it to make tools for rigorous
quantification, sensitivity analyses, and cross-method benchmarking available
to practitioners.
