Abstract. Skeletal parallel programming enables us to develop parallel programs easily by composing ready-made components called skeletons. However, a simplycomposed skeleton program often lacks efficiency due to overheads of intermediate data structures and communications. Many studies have focused on optimizations by fusing successive skeletons to eliminate the overheads. Existing fusion transformations, however, are too general to achieve adequate efficiency for some classes of problems. Thus, a specific fusion optimization is needed for a specific class. In this paper, we propose a strategy for domain-specific optimization of skeleton programs. In this strategy, one starts with a normal form that abstracts the programs of interest, then develops fusion rules that transform a skeleton program into the normal form, and finally makes efficient parallel implementation of the normal form. We illustrate the strategy with a case study: optimization of skeleton programs involving neighbor elements, which is often seen in scientific computations. 1