HAMA: An Efficient Matrix Computation with the MapReduce Framework
Cloud Computing Technology and Science（2010）
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.
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Key words
mapreduce framework,state-of-the-art framework,different computation engine,important role,efficient matrix computation,high-level matrix computation primitive,case study approach,parallel systems literature,various scientific computation,computation tool,scientific computation,scientific computing,iterative methods,matrix computation,cloud computing,software architecture,iterative algorithm,mpi,scalability,parallel processing,sparse matrices,parallel systems,engines
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