Tree structured data processing on GPUs

Abstract

In order to reduce the computing time for processing large tree-structured data sets, parallel processing has been used. Recently, research has been done on parallel computing of tree-structured data on Graphics Processing Units (GPUs). GPU device cannot directly access the tree structured data on hard disks which is commonly stored as objects or linked-lists. So, it is required to copying this tree structured data from hard disk to device memory for the computation and copying tree structured data in its normal structure is very costly because of lots of pointers overhead. Existing tree data structures on GPUs are commonly applied to storing a particular kind of tree, and support limited types of tree traversals. In this work, a tree data structure is proposed to store different kind of trees as a linear data structure (fast in copying). The proposed data structure is applied on general trees and binary trees and supports four common types of tree traversals: pre-order, post-order, in-order and breadth-first traversals. Therefore, most of the tree algorithms can be implemented on GPUs by using this proposed data structure. The results show that the proposed data structure is successfully implemented for all the traversals for binary as well as general trees.