  The extension of persistent homology to multi-parameter setups is an
algorithmic challenge. Since most computation tasks scale badly with the size
of the input complex, an important pre-processing step consists of simplifying
the input while maintaining the homological information. We present an
algorithm that drastically reduces the size of an input. Our approach is an
extension of the chunk algorithm for persistent homology (Bauer et al.,
Topological Methods in Data Analysis and Visualization III, 2014). We show that
our construction produces the smallest multi-filtered chain complex among all
the complexes quasi-isomorphic to the input, improving on the guarantees of
previous work in the context of discrete Morse theory. Our algorithm also
offers an immediate parallelization scheme in shared memory. Already its
sequential version compares favorably with existing simplification schemes, as
we show by experimental evaluation.
