Abstract

Automatic parallelization of nested loops, based on a mathematical model, the polytope model, has been improved significantly over the last decade: state-of-theart methods allow flexible distributions of computations in space and time, which lead to high-quality parallelism. However, these methods have not found their way into practical parallelizing compilers due to the lack of code generation schemes which are able to deal with the new-found flexibility. To close this gap is the purpose of this paper. 1. Introduction In recent years, methods for automatic parallelization of nested loops based on a mathematical model, the polytope model [9, 12], have been improved significantly. The focus has been on identifying good schedules, i.e., distributions of computations in time, e.g., [6, 8], and allocations, i.e., distributions of computations in space, e.g., [5, 14]. Thus, the space-time mapping, i.e., the combination of schedule and allocation, derived by state-of-the-art techniques oft...

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