This paper describes a new-generation vector parallel supercomputer, NEC SX-9 system. The SX-9 processor has an outstanding core to achieve over 100Gflop/s, and a software-controllable on-chip cache to keep the high ratio of the memory bandwidth to the floating-point operation rate. Moreover, its large SMP nodes of 16 vector processors with 1.6Tflop/s performance and 1TB memory are connected with dedicated network switches, which can achieve inter-node communication at 128GB/s per direction. The sustained performance of the SX-9 processor is evaluated using six practical applications in comparison with conventional vector processors and the latest scalar processor such as Nehalem-EP. Based on the results, this paper discusses the performance tuning strategies for new-generation vector systems. An SX-9 system of 16 nodes is also evaluated by using the HPC challenge benchmark suite and a CFD code. Those evaluation results clarify the highest sustained performance and scalability of the SX-9 system.

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.

We describe the overall role of soft computing (SC) in signal processing and pattern recognition (SPPR) with specific applications to biomedical engineering, geoscience for mining and civil engineering human interfaces, and image processing. Detection of characteristic points in an electrocardiogram to implement an advanced ECG analyzer is presented which is carried out using both conventional SPPR techniques and self-organizing neural networks. Successful technologies for monitoring a geostructure by supervised and self-organizing neural networks are described. Identification of a freehand drawing by a combination of fuzzy logic and neural networks is also described. Moreover, application of fuzzy logic to image segmentation is presented. Finally, innovation of SPPR using SC technologies is discussed.