This work presents a novel strategy for the parallelization of applications
containing sparse matrix references using the data-paxallel paradigm. Our
approach is a first stop to converge to the automatic parallelization by re
ducing the number of directives on code. We have used the semantical relati
onship of vectors composing a high-level data structure to enhance the perf
ormance of the parallel code, applying a sparse privatization and a multi-l
oop analysis. We also study the building/updating of a sparse matrix at run
-time, solving the problem of using pointers and some levels of indirection
s on the left hand side. A detailed analysis about several temporary buffer
s useful for sparse communications is described in this paper. The evaluati
on of our strategy has been performed on a Cray T3E with sparse matrix tran
sposition algorithm as a case of study.