  The {\em compressed stack} is a data structure designed by Barba {\em et al.}
(Algorithmica 2015) that allows to reduce the amount of memory needed by an
algorithm (at the cost of increasing its runtime). In this paper we introduce
the first implementation of this data structure and make its source code
publicly available.
  Together with the implementation we analyze the performance of the compressed
stack. In our synthetic experiments, considering different test scenarios and
using data sizes ranging up to $2^{30}$ elements, we compare it with the
classic (uncompressed) stack, both in terms of runtime and memory used.
  Our experiments show that the compressed stack needs significantly less
memory than the usual stack (this difference is significant for inputs
containing $2000$ or more elements). Overall, with a proper choice of
parameters, we can save a significant amount of space (from two to four orders
of magnitude) with a small increase in the runtime ($2.32$ times slower on
average than the classic stack). These results holds even in test scenarios
specifically designed to be challenging for the compressed stack.
