Data dependence analysis for the parallelization of numerical tree codes

Abstract. Data dependence analysis for automatic parallelization of sequential tree codes is discussed. Hierarchical numerical algorithms often use tree data structures for unbalanced, adaptively and dynamically created trees. Moreover, such codes often do not follow a strict divide and conquer concept, but introduce some geometric neighborhood data dependence in addition to parent-children dependencies. Hence, recognition mechanisms and hierarchical partition strategies of trees are not sufficient for automatic parallelization. Generic tree traversal operators are proposed as a domain specific language. Additional geometric data dependence can be specified by code annotation. A code transformation system with data dependence analysis is implemented, which generates several versions of parallel codes for different programming models.