Real-time 30-second-refresh numerical weather prediction (NWP) was performed with exclusive use of 11,580 nodes (~7%) of supercomputer Fugaku during Tokyo Olympics and Paralympics in 2021. Total 75,248 forecasts were disseminated in the 1-month period mostly stably with time-to-solution less than 3 minutes for 30-minute forecast. Japan's Big Data Assimilation (BDA) project developed the novel NWP system for precise prediction of hazardous rains toward solving the global climate crisis. Compared with typical 1-hour-refresh systems, the BDA system offered two orders of magnitude increase in problem size and revealed the effectiveness of 30-second refresh for highly nonlinear, rapidly evolving convective rains. To achieve the required time-to-solution for real-time 30-second refresh with high accuracy, the core BDA software incorporated single precision and enhanced parallel I/O with properly selected configurations of 1000 ensemble members and 500-m-mesh weather model. The massively parallel, I/O intensive real-time BDA computation demonstrated a promising future direction.

High utilization of HPC system resources under constraints in electric power consumption or I/O workload is one of the primary goals to deal with high demand from application users. Utilization of CPU and memory, which is tightly related to electric power consumption, is counterpart metric of I/O activities in most HPC jobs. Towards higher utilization of HPC systems under restriction in management for electric power consumption and I/O activities, we need to care not to have hot-spots in power consumption or I/O operations because such situation leads to unstable system operation by exceeding capability of electric power supply or the I/O subsystem in such hot-spots. Analysis of a huge scale of log data collected from the K computer has revealed high correlation between I/O activities and CPU and memory utilization in some specific compute node layouts, showing unique characteristics of HPC jobs such as computation intensive or I/O-intensive. It has turned out that classifying jobs in terms of required electric power can divide into two groups, jobs consuming high electric power and I/O-intensive jobs. We have succeeded in job classification by achieving high correctness using machine learning approach, and we have confirmed effectiveness of the classification towards power-aware system operation in our next HPC system, the supercomputer Fugaku.

MPI-IO has been used in an internal I/O interface layer of HDF5 or PnetCDF, where collective MPI-IO plays a big role in parallel I/O to manage a huge scale of scientific data. However, existing collective MPI-IO optimization named two-phase I/O has not been tuned enough for recent supercomputers consisting of mesh/torus interconnects and a huge scale of parallel file systems due to lack of topology-awareness in data transfers and optimization for parallel file systems. In this paper, we propose I/O throttling and topology-aware stepwise data aggregation in two-phase I/O of ROMIO, which is a representative MPI-IO library, in order to improve collective MPI-IO performance even if we have multiple processes per compute node. Throttling I/O requests going to a target file system mitigates I/O request contention, and consequently I/O performance improvements are achieved in file access phase of two-phase I/O. Topology-aware aggregator layout with paying attention to multiple aggregators per compute node alleviates contention in data aggregation phase of two-phase I/O. In addition, stepwise data aggregation improves data aggregation performance. HPIO benchmark results on the K computer indicate that the proposed optimization has achieved up to about 73% and 39% improvements in write performance compared with the original implementation using 12,288 and 24,576 processes on 3,072 and 6,144 compute nodes, respectively.

Real space DFT (RSDFT) is a simulation technique most suitable for massively-parallel architectures to perform first-principles electronic-structure calculations based on density functional theory. We here report unprecedented simulations on the electron states of silicon nanowires with up to 107,292 atoms carried out during the initial performance evaluation phase of the K computer being developed at RIKEN.

The high-resolution direct numerical simulations (DNSs) of incompressible turbulence with numbers of grid points up to 40963 have been executed on the Earth Simulator (ES). The DNSs are based on the Fourier spectral method, so that the equation for mass conservation is accurately solved. In DNS based on the spectral method, most of the computation time is consumed in calculating the three-dimensional (3D) Fast Fourier Transform (FFT), which requires huge-scale global data transfer and has been the major stumbling block that has prevented truly high-performance computing. By implementing new methods to efficiently perform the 3D-FFT on the ES, we have achieved DNS at 16.4 Tflops on 20483 grid points. The DNS yields an energy spectrum exhibiting a wide inertial subrange, in contrast to previous DNSs with lower resolutions, and therefore provides valuable data for the study of the universal features of turbulence at large Reynolds number.

This paper describes a preliminary study on a merging control algorithm of vehicles on highways using intervehicle communication. Merging control of a single vehicle into a platoon of vehicles is achieved by mapping of a vehicle onto a ramp or a main line to generate a virtual vehicle with which a vehicle is longitudinally controlled. Simulation studies show feasibility of the algorithm.