Data centric framework for large-scale high-performance parallel computation

Supercomputer architectures are being upgraded using different level of parallelism to improve computing performance. This makes it difficult for scientists to develop high performance code in a short time. From the viewpoint of productivity and software life cycle, a concise yet effective infrastructure is required to achieve parallel processing. In this paper, we propose a usable building block framework to build parallel applications on large-scale Cartesian data structures. The proposed framework is designed such that each process in a simulation cycle can easily access the generated data files with usable functions. This framework enables us to describe parallel applications with fewer lines of source code, and hence, it contributes to the productivity of the software. Further, this framework was considered for improving performance, and it was confirmed that the developed flow simulator based on this framework demonstrated considerably excellent weak scaling performance on the K computer.