A multi-process library for multi-agent and spatial simulation

Integrating sensor networks in cloud computing gives new opportunities of using as many cloud-compute nodes as necessary to analyze real-time sensor data on the fly. However, most cloud services for parallelization such as OpenMP, MPI, and MapReduce are not always fitted to on-the-fly sensor-data analyses that are implemented as model-based entity-based, and multi-agent simulations. To address this semantic gap between analyzing algorithms and their actual implementations, we are developing MASS: a library for multi-agent spatial simulation that composes a user application of distributed array elements and multi-agents, each representing an individual simulation place or an active entity. All computation is enclosed in each of elements and agents that are automatically distributed over different computing nodes. Their communication is then scheduled as periodical data exchanges among those entities using their logical indices. We are currently implementing a multi-process and a multi-threaded version of the MASS library, both to be combined in a single version in the near future. This paper focuses on an implementation and preliminary performance of the multi-process version.