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.

We have used data learning and low-precision computation to develop an implicit solver that demonstrates high performance up to 152,352 computer nodes (609,408 MPI processes × 12 OpenMP threads = 7,312,896 parallel computation) and conducted an unprecedented ultra-large-scale analysis of ultra-high-fidelity fault-structure systems using nonlinear dynamic finite element analysis on three-dimensional low-order unstructured elements. The developed solver achieved 25.45-fold speedup from the state of the art solver on Fugaku and attained weak scaling efficiency of 93.7% from 9.391 billion [email protected] computer nodes to 1.201 trillion [email protected],984 computer nodes on performance measurement problems. Moreover, a realistic 324 billion DOF application example, which is difficult to obtain performance for, was computed in high performance. Since the developed solver is based on a highly generalizable algorithm, it is expected to contribute not only to earthquake simulation on Fugaku but also to the enhancement of similar applications in other fields and on other supercomputers.

Summary On the verge of the convergence between high‐performance computing and Big Data processing, it has become increasingly prevalent to deploy large‐scale data analytics workloads on high‐end supercomputers. Such applications often come in the form of complex workflows with various different components, assimilating data from scientific simulations as well as from measurements streamed from sensor networks, such as radars and satellites. For example, as part of the Flagship 2020 (post‐K) supercomputer project of Japan, RIKEN is investigating the feasibility of a highly accurate weather forecasting system that would provide a real‐time outlook for severe guerrilla rainstorms. One of the main performance bottlenecks of this application is the lack of efficient communication among workflow components, which currently takes place over the parallel file system.In this paper, we present an initial study of a direct communication framework designed for complex workflows that eliminates unnecessary file I/O among components. Specifically, we propose an I/O arbitration layer that provides direct parallel data transfer (both synchronous and asynchronous) among job components that rely on the netCDF interface for performing I/O operations. Our solution requires only minimal modifications to application code. Moreover, we propose a configuration file–based approach that allows users to specify the desired data transfer pattern among workflow components, offering a general solution for different application contexts. We present a preliminary evaluation of the proposed framework on the K Computer (running on up to 4800 compute nodes) using RIKEN's experimental weather forecasting workflow as a case study.

We propose a cost-effective online nesting procedure (CONeP) for regional atmospheric models to improve computational efficiency. The conventional procedure of online nesting is ineffective because computations are executed sequentially for each domain, and it does not enable users freely to determine the number of computational nodes. However, CONeP can completely avoid this limitation through three actions: 1) splitting the processes into multiple subgroups; 2) making each subgroup manage just one domain; and 3) executing the computations for each domain simultaneously. Since users can assign an optimal number of nodes to each domain, the model with CONeP is computationally efficient. We demonstrate the computational advantage of CONeP over the conventional procedure, comparing the elapsed times with both procedures on a supercomputer. The elapsed time with CONeP is markedly shorter than that observed with the conventional procedure using the same number of computational nodes. This advantage becomes more significant as the number of nesting domains increases.

Stencil computation has many applications in science and engineering, thus many optimization techniques such as temporal blocking have been developed. They are, however, rarely used in real-world applications, since a large amount of careful programming is required for even the simplest of stencils. We introduce Formura, a domain specific language that provides easy access to optimized stencil computations. Higher-order integration schemes can be defined using mathematical notations. Formura generates C code with MPI calls and performs autotuning. Hence its performance is portable to most distributed-memory computers. We show the scientific applicability of Formura by performing magnetohydrodynamics (MHD) and belowground biology simulations. Ability to reach bytes-per-flops ratio only attainable by temporal blocking is demonstrated. We also demonstrate scaling up to the full nodes of the K computer, with 1.184 Pflops, 11.62% floating-pointoperation efficiency, and 31.26% memory throughput efficiency.

Following the invention of the telegraph, electronic computer, and remote sensing, “big data” is bringing another revolution to weather prediction. As sensor and computer technologies advance, orders of magnitude bigger data are produced by new sensors and high-precision computer simulation or “big simulation.” Data assimilation (DA) is a key to numerical weather prediction (NWP) by integrating the real-world sensor data into simulation. However, the current DA and NWP systems are not designed to handle the “big data” from next-generation sensors and big simulation. Therefore, we propose “big data assimilation” (BDA) innovation to fully utilize the big data. Since October 2013, the Japan's BDA project has been exploring revolutionary NWP at 100-m mesh refreshed every 30 s, orders of magnitude finer and faster than the current typical NWP systems, by taking advantage of the fortunate combination of next-generation technologies: the 10-petaflops K computer, phased array weather radar, and geostationary satellite Himawari-8. So far, a BDA prototype system was developed and tested with real-world retrospective local rainstorm cases. This paper summarizes the activities and progress of the BDA project, and concludes with perspectives toward the post-petascale supercomputing era.