Evaluating MapReduce frameworks for iterative Scientific Computing applications

Scientific Computing deals with solving complex scientific problems by applying resource-hungry computer simulation and modeling tasks on-top of supercomputers, grids and clusters. Typical scientific computing applications can take months to create and debug when applying de facto parallelization solutions like Message Passing Interface (MPI), in which the bulk of the parallelization details have to be handled by the users. Frameworks based on the MapReduce model, like Hadoop, can greatly simplify creating distributed applications by handling most of the parallelization and fault recovery details automatically for the user. However, Hadoop is strictly designed for simple, embarrassingly parallel algorithms and is not suitable for complex and especially iterative algorithms often used in scientific computing. The goal of this work is to analyze alternative MapReduce frameworks to evaluate how well they suit for solving resource hungry scientific computing problems in comparison to the assumed worst (Hadoop MapReduce) and best case (MPI) implementations for iterative algorithms.