Industrial applications tend to rely increasingly on large datasets for
regular operations. In order to facilitate that need, we unite the
increasingly available hardware resources with fundamental problems found in
classical algorithms. We show solutions to the following problems: power
flow and island detection in power networks, and the more general graph
sparsification. At their core lie respectively algorithms for solving
systems of linear equations, graph connectivity and matrix multiplication,
and spectral sparsification of graphs, which are applicable on their own to
a far greater spectrum of problems. The novelty of our approach lies in
developing the first open source and distributed solutions, capable of
handling large datasets. Such solutions constitute a toolkit, which, aside
from the initial purpose, can be used for the development of unrelated
applications and for educational purposes in the study of distributed
algorithms.