Evaluating SPLASH-2 Applications Using MapReduce

MapReduce has been prevalent for running data-parallel applications. By hiding other non-functionality parts such as parallelism, fault tolerance and load balance from programmers, MapReduce significantly simplifies the programming of large clusters. Due to the mentioned features of MapReduce above, researchers have also explored the use of MapReduce on other application domains, such as machine learning, textual retrieval and statistical translation, among others. In this paper, we study the feasibility of running typical supercomputing applications using the MapReduce framework. We port two applications (Water Spatial and Radix Sort) from the Stanford SPLASH-2 suite to MapReduce. By completely evaluating them in Hadoop, an open-source MapReduce framework for clusters, we analyze the major performance bottleneck of them in the MapReduce framework. Based on this, we also provide several suggestions in enhancing the MapReduce framework to suite these applications.