Smart: a MapReduce-like framework for in-situ scientific analytics

In-situ analytics has lately been shown to be an effective approach to reduce both I/O and storage costs for scientific analytics. Developing an efficient in-situ implementation, however, involves many challenges, including parallelization, data movement or sharing, and resource allocation. Based on the premise that MapReduce can be an appropriate API for specifying scientific analytics applications, we present a novel MapReduce-like framework that supports efficient in-situ scientific analytics, and address several challenges that arise in applying the MapReduce idea for in-situ processing. Specifically, our implementation can load simulated data directly from distributed memory, and it uses a modified API that helps meet the strict memory constraints of in-situ analytics. The framework is designed so that analytics can be launched from the parallel code region of a simulation program. We have developed both time sharing and space sharing modes for maximizing the performance in different scenarios, with the former even avoiding any copying of data from simulation to the analytics program. We demonstrate the functionality, efficiency, and scalability of our system, by using different simulation and analytics programs, executed on clusters with multi-core and many-core nodes.