High Performance Fully In-Gpu Parallel Inductive Learner in Description Logic

This article presents a high performance fully in-GPU inductive logic learning algorithm. The proposed algorithm performs all inductive logic learning activities such as hypothesis search and hypothesis evaluation, fully in-GPU with zero CPU involvement. The core contribution of this work, is a high performance fully in-GPU ILP (inductive logic programming) algorithm in description logic (DL), that also supports parallel hypothesis search. The proposed learner addresses the ILP research gap of non-existing GPU-based parallel hypothesis search approaches that target more expressive logics like FOL (first order logic) and DL. Within the proposed learner, we also propose a set of high performance GPU-based hypothesis evaluation algorithms. In addition, the proposed learner is based on the DL-Learner[ 11 ] (the state-of-the-art in DL-based ILPs), with which the proposed learner incorporates many of the DL-Learner’s search strategies – like deterministic ordering of hypotheses operands, to reduce the size of the search space. According to experimental results on four datasets, the proposed in-GPU learner is always faster than the DL-Learner on all datasets; for example, in the IMDB dataset, the proposed learner is faster by up to ~ 18 folds than the DL-Learner (represented using SPILDL at BeamWidth = 1). When considering the average of all hypothesis search beam widths, the proposed learner is always faster than SPILDL[ 6 ] on all datasets; SPILDL is our high performance CPU-based parallel learner in DL. In the Dunnhumby Retail dataset, on average, the proposed learner is faster than SPILDL by ~ 6.55 folds. In terms of worst case scenario, it is demonstrated through parallel search at certain beam width values. For example, at BeamWidth = 8 in the trains dataset, SPILDL is faster than the proposed learner by ~ 2.4 folds.