Comparing implementations of near-data computing with in-memory mapreduce workloads

Abstract

The emergence of 3D stacking and the imminent release of Micron's Hybrid Memory Cube (HMC) device have made it more practical to move computation near memory. This work presents a detailed analysis of in-memory MapReduce in the context of near-data computing (NDC). MapReduce is a good fit for NDC because it is embarrassingly parallel and has highly localized memory accesses. This article considers two NDC architectures: one that exploits HMC devices and one that does not. It thus provides insight on the benefits of different NDC approaches and quantifies the potential for improvement for an important emerging big-data workload.