Scientific databases must be able to efficiently run subset
retrievals of multi-dimensional data sets.
If the data sets are very large significant retrieval speedups
can be obtained via parallelism.
In this paper we present a new parallel distributed shared
nothing R-tree architecture.
To the best of our knowledge this is the first significant
experimental study demonstrating practical application
of parallel R-trees in a shared nothing environment.
We argue that our new architecture is better than those proposed
in the past and provide experimental results demonstrating
actual speedups for several synthetic and real data sets.
In addition, we conduct experimental studies to investigate
the effect of several declustering strategies
and communication parameters.