Subdividing the iteration space of a loop into blocks or tiles with a fixed maximum size has several advantages. Tiles become a natural candidate as the unit of work for parallel task scheduling. Synchronization between processors can be done between tiles, reducing synchronization frequency (at some loss of potential parallelism).The shape and size of a tile can be optimized to take advantage of memory locality for memory hierarchy utilization. Vectorization and register locality naturally fits into the optimization within a tile, while parallelization and cache locality fits into optimization between tiles,

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Compilers for vector or multiprocessor computers must have certain optimization features to successfully generate parallel code.

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Linear induction variable detection is usually associated with the strength reduction optimization. For restructuring compilers, effective data dependence analysis requires that the compiler detect and accurately describe linear and nonlinear induction variables as well as more general sequences. In this article we present a practical technique for detecting a broader class of linear induction variables than is usually recognized, as well as several other sequence forms, including periodic, polynomial, geometric, monotonic, and wrap-around variables. Our method is based on Factored Use-Def (FUD) chains, a demand-driven representation of the popular Static Single Assignment (SSA) form. In this form, strongly connected components of the associated SSA graph correspond to sequences in the source program: we describe a simple yet efficient algorithm for detecting and classifying these sequences. We have implemented this algorithm in Nascent, our restructuring Fortran 90+ compiler, and we present some results showing the effectiveness of our approach.