  Computer science is also an experimental science. This is particularly the
case for parallel computing, which is in a total state of flux, and where
experiments are necessary to substantiate, complement, and challenge
theoretical modeling and analysis. Here, experimental work is as important as
are advances in theory, that are indeed often driven by the experimental
findings. In parallel computing, scientific contributions presented in research
articles are therefore often based on experimental data, with a substantial
part devoted to presenting and discussing the experimental findings. As in all
of experimental science, experiments must be presented in a way that makes
reproduction by other researchers possible, in principle. Despite appearance to
the contrary, we contend that reproducibility plays a small role, and is
typically not achieved. As can be found, articles often do not have a
sufficiently detailed description of their experiments, and do not make
available the software used to obtain the claimed results. As a consequence,
parallel computational results are most often impossible to reproduce, often
questionable, and therefore of little or no scientific value. We believe that
the description of how to reproduce findings should play an important part in
every serious, experiment-based parallel computing research article. We aim to
initiate a discussion of the reproducibility issue in parallel computing, and
elaborate on the importance of reproducible research for (1) better and sounder
technical/scientific papers, (2) a sounder and more efficient review process
and (3) more effective collective work. This paper expresses our current view
on the subject and should be read as a position statement for discussion and
future work. We do not consider the related (but no less important) issue of
the quality of the experimental design.
