Improving node-level mapreduce performance using processing-in-memory technologies

Abstract

Processing-in-Memory (PIM) is the concept of moving computation as close as possible to memory. This decreases the need for the movement of data between central processor and memory system, hence improves energy efficiency from the reduced memory traffic. In this paper we present our approach on how to embed processing cores in 3D-stacked memories, and evaluate the use of such a system for Big Data analytics. We present a simple server architecture, which employs several energy efficient PIM cores in multiple 3D-DRAM units where the server acts as a node of a cluster for Big Data analyses utilizing MapReduce programming framework. Our preliminary analyses show that on a single node up to 23% energy savings on the processing units can be achieved while reducing execution time by up to 8.8%. Additional energy savings can result from simplifying the system memory buses. We believe such energy efficient systems with PIM capability will become viable in the near future because of the potential to scale the memory wall.