A classification of nested loops parallelization algorithms

Compares three nested loops parallelization algorithms (Allen and Kennedy´s algorithm, Wolf and Lam´s algorithm and Darte and Vivien´s algorithm) that use different representations of distance vectors as input. The authors identify the concepts that make them similar or different. The authors study the optimality of each with respect to the dependence analysis it uses. The authors propose well-chosen examples that illustrate the power and limitations of the three algorithms. This study permits the authors to identify which algorithm is the most suitable for a given representation of dependences