Abstract
Due to the rapid popularity of multi-core processors systems, the parallelization of XPath queries in shared memory multi-core systems has been studied gradually. Existing work developed some parallelization methods based on cost estimation and static mapping, which could be seen as a logical optimization of parallel query plan. However, static mapping may result in load imbalance that hurts the overall performance, especially when nodes in XML are not evenly distributed. In this paper, we solve the problem from another view using parallelizing techniques. We use dynamic mapping to improve XPath query performance, which can achieve better load balance no matter what XML document is queried. Compared with static mapping, dynamic mapping is a more general method. We first design a parallel XPath query algebra called PXQA (ParallelXPath Query Algebra) to explain the parallel query plan. And second, using PXQA we extract the task-dependence graph to define which operations can be executed in parallel and help analyze the overheads of dynamic mapping. At last, we discuss how to do the data partition based on dynamic mapping in accordance with the runtime situations adaptively. Experimental results show that the adaptive runtime XPath queries parallelization achieves a good performance in shared memory multi-core systems.
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