HAMA: An Efficient Matrix Computation with the MapReduce Framework

The state-of-the-art framework providing high-level matrix computation primitives with MapReduce is explored through the case study approach, and these primitives are demonstrated with different computation engines to show the performance and scalability.

Abstract

Various scientific computations have become so complex, and thus computation tools play an important role. In this paper, we explore the state-of-the-art framework providing high-level matrix computation primitives with MapReduce through the case study approach, and demonstrate these primitives with different computation engines to show the performance and scalability. We believe the opportunity for using MapReduce in scientific computation is even more promising than the success to date in the parallel systems literature.