The present study deals with the Vlasov simulation code, which solves the first-principle kinetic equations called the Vlasov equation for space plasma. In the present study, a five-dimensional Vlasov code with two spatial dimension and three velocity dimensions is parallelized with two methods, the flat-MPI and the MPI-OpenMP hybrid parallelism. The two types of the parallel Vlasov code are benchmarked on massively-parallel supercomputer Fujitsu FX100, which has been developed with the second-generation post architecture of the K computer in Japan. In the present performance comparison, we vary the number of threads per nodes from 1 (the flat-MPI parallelism) to 32. The result shows that the OpenMP-MPI hybrid parallelism outperforms the flat-MPI for any number of compute nodes. There is an optimum number of threads per nodes depending on the number of compute nodes. It is shown that the optimum number of threads per node becomes larger on a larger number of compute nodes. This is because the communication time of an MPI collective communication subroutine, which is used for convergence check of iterative methods, can be reduced by decreasing the total number of processes with the OpenMP-MPI hybrid parallelism.

This paper introduces a region of interest (ROI) sharing protocol for robots, each of which observes different regions in the same scene. This type of observation is generally referred to as “distributed sensing.” The ROI sharing protocol is implemented based on a concept of semantic stability, which helps to find a stable context by selecting sufficient information from an image or a scene. The semantic stability is formulated based on semantic relatedness and the information gap in the relations found in the ROI. Moreover, the necessary and sufficient information is determined by selecting the best ROI from a given image. Finally, the best ROI is shared between the robots for making cooperation. Experimental results show that the ROI sharing is efficient in terms of task performance and robustness.