Performance Evaluation on Parallel Speculation-Based Construction of a Binary Search Tree

Abstract

Abstract Binary search trees (BSTs) are one of the most important data structures in the field of computer science. We may easily write a parallel construction program of a BST by extending the sequential algorithm straightly. However, in such conventional approaches, the order of nodes inserted into a BST is determined dynamically, depending on the occasional state of the parallel thread execution. It results in a BST with a different structure (node position) generated on every execution of the parallel program. On the other hand, we have been developing parallel construction schemes of the BST with the same structure as a BST generated by the sequential algorithm. One is the speculatively parallel construction of a BST. And another is the purely (non-speculatively) parallel construction, but it was derived through the concept of thread-level speculation. This paper evaluates the performances of those construction schemes on several types of shared-memory multiprocessors. For the large enough size of BST, our new parallel programs can construct a BST with always the same structure on a little lower or sometimes higher performance than the program that makes a BST with a different structure on every execution. And in contrast with the general expectation that simply enlarging the size of parallel tasks increases misspeculation and damages the performance, we found that it sometimes enhances the performance of speculatively parallel execution.