A New Data Layout For Set Intersection on GPUs

Abstract—Set intersection is the core in a variety of problems, e.g. frequent itemset mining and sparse boolean matrix multiplication. It is well-known that large speed gains can, for some computational problems, be obtained by using a graphics processing unit (GPU) as a massively parallel computing device. However, GPUs require highly regular control flow and memory access patterns, and for this reason previous GPU methods for intersecting sets have used a simple bitmap representation. This representation requires excessive space on sparse data sets. In this paper we present a novel data layout, BATMAP, that is particularly well suited for parallel processing, and is compact even for sparse data. Frequent itemset mining is one of the most important applications of set intersection. As a case-study on the potential of BATMAPs we focus on frequent pair mining, which is a core special case of frequent itemset mining. The main finding is that our method is able to achieve speedups ove...

• Type: Journal
• Year: 2011
• Where: CORR
• Authors: Rasmus Resen Amossen, Rasmus Pagh
• Keywords: Boolean Matrix Multiplication, CORR 2011, Education, Frequent Itemset Mining, Graphics Processing Unit