Mapping data mining algorithms on a gpu architecture: A study

Data mining algorithms are designed to extract information from a huge amount of data in an automatic way. The datasets that can be analysed with these techniques are gathered from a variety of domains, from business related fields to HPC and supercomputers. The datasets continue to increase at an exponential rate, so research has been focusing on parallelizing different data mining techniques. Recently, GPU hybrid architectures are starting to be used for this task. However the data transfer rate between CPU and GPU is a bottleneck for the applications dealing with large data entries exhibiting numerous dependencies. In this paper we analyse how efficient data mining algorithms can be mapped on these architectures by extracting the common characteristics of these methods and by looking at the communication patterns between the main memory and the GPU's shared memory. We propose an experimental study for the performance of memory systems on GPU architectures when dealing with data mining algorithms and we also advance performance model guidelines based on the observations.