As the datasets used to fuel modern scientific discovery grow increasingly large, they become increasingly difficult to manage using conventional software. Parallel database management systems (DBMSs) and massive-scale data processing systems such as MapReduce hold promise to address this challenge. However, since these systems have not been expressly designed for scientific applications, their efficacy in this domain has not been thoroughly tested. In this paper, we study the performance of these engines in one specific domain: massive astrophysical simulations. We develop a use case that comprises five representative queries. We implement this use case in one distributed DBMS and in the Pig/Hadoop system. We compare the performance of the tools to each other and to hand-written IDL scripts. We find that certain representative analyses are easy to express in each engine's highlevel language and both systems provide competitive performance and improved scalability relative to current IDL-based methods.

The Convex SPP-1000, the most recent SPC, is distinguished by a true global shared memory capability based on the first commercial version of directory based cache coherence mechanisms and SCI protocol. The system was evaluated at NASA/GSFC in the Beta-test environment using three classes of operational experiments targeting earth and space science applications. A multiple program workload tested job-stream level parallelism. Synthetic programs measured overhead costs of barrier, fork-join, and message passing synchronization primitives. An efficient tree-code version of an N-body simulation revealed scaling properties and measured the overall efficiency. This paper presents the results of this study and provides the earliest published evaluation of this new scalable architecture.>