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The Earth Simulator (the + earth_simulator)

Distribution by Scientific Domains

Earth and Environmental Science	33%

Selected Abstracts

A performance comparison between the Earth Simulator and other terascale systems on a characteristic ASCI workload,

CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 10 2005
Darren J. Kerbyson
Abstract This work gives a detailed analysis of the relative performance of the recently installed Earth Simulator and the next top four systems in the Top500 list using predictive performance models. The Earth Simulator uses vector processing nodes interconnected using a single-stage, cross-bar network, whereas the next top four systems are built using commodity based superscalar microprocessors and interconnection networks. The performance that can be achieved results from an interplay of system characteristics, application requirements and scalability behavior. Detailed performance models are used here to predict the performance of two codes representative of the ASCI workload, namely SAGE and Sweep3D. The performance models encapsulate fully the behavior of these codes and have been previously validated on many large-scale systems. One result of this analysis is to size systems, built from the same nodes and networks as those in the top five, that will have the same performance as the Earth Simulator. In particular, the largest ASCI machine, ASCI Q, is expected to achieve a similar performance to the Earth Simulator on the representative workload. Published in 2005 by John Wiley & Sons, Ltd. [source]

Evaluating high-performance computers,

CONCURRENCY AND COMPUTATION: PRACTICE & EXPERIENCE, Issue 10 2005
Jeffrey S. Vetter
Abstract Comparisons of high-performance computers based on their peak floating point performance are common but seldom useful when comparing performance on real workloads. Factors that influence sustained performance extend beyond a system's floating-point units, and real applications exercise machines in complex and diverse ways. Even when it is possible to compare systems based on their performance, other considerations affect which machine is best for a given organization. These include the cost, the facilities requirements (power, floorspace, etc.), the programming model, the existing code base, and so on. This paper describes some of the important measures for evaluating high-performance computers. We present data for many of these metrics based on our experience at Lawrence Livermore National Laboratory (LLNL), and we compare them with published information on the Earth Simulator. We argue that evaluating systems involves far more than comparing benchmarks and acquisition costs. We show that evaluating systems often involves complex choices among a variety of factors that influence the value of a supercomputer to an organization, and that the high-end computing community should view cost/performance comparisons of different architectures with skepticism. Published in 2005 by John Wiley & Sons, Ltd. [source]

Transport and environmental temperature variability of eggs and larvae of the Japanese anchovy (Engraulis japonicus) and Japanese sardine (Sardinops melanostictus) in the western North Pacific estimated via numerical particle-tracking experiments

FISHERIES OCEANOGRAPHY, Issue 2 2009
SACHIHIKO ITOH
Abstract Numerical particle-tracking experiments were performed to investigate the transport and variability in environmental temperature experienced by eggs and larvae of Pacific stocks of the Japanese anchovy (Engraulis japonicus) and Japanese sardine (Sardinops melanostictus) using high-resolution outputs of the Ocean General Circulation Model for the Earth Simulator (OFES) and the observed distributions of eggs collected from 1978 to 2004. The modeled anchovy individuals tend to be trapped in coastal waters or transported to the Kuroshio,Oyashio transition region. In contrast, a large proportion of the sardines are transported to the Kuroshio Extension. The egg density-weighted mean environmental temperature until day 30 of the experiment was 20,24°C for the anchovy and 17,20°C for the sardine, which can be explained by spawning areas and seasons, and interannual oceanic variability. Regression analyses revealed that the contribution of environmental temperature to the logarithm of recruitment per spawning (expected to have a negative relationship with the mean mortality coefficient) was significant for both the anchovy and sardine, especially until day 30, which can be regarded as the initial stages of their life cycles. The relationship was quadratic for the anchovy, with an optimal temperature of 21,22°C, and linear for the sardine, with a negative coefficient. Differences in habitat areas and temperature responses between the sardine and anchovy are suggested to be important factors in controlling the dramatic out-of-phase fluctuations of these species. [source]

An improved PDF cloud scheme for climate simulations

THE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 651 2010
Akira Kuwano-Yoshida
Abstract An efficient grid-scale cloud scheme for climate simulation is implemented in the atmospheric general circulation model for the Earth Simulator (AFES). The new cloud scheme uses statistical partial condensation using joint-Gaussian probability distribution functions (PDFs) of the liquid water potential temperature and total water content, with standard deviations estimated by the moist Mellor,Yamada level-2 turbulence scheme. It also adopts improved closure parameters based on large-eddy simulations and a revised mixing length that varies with the stability and turbulent kinetic energy. These changes not only enable better representation of low-level boundary layer clouds, but also improve the atmospheric boundary layer structure. Sensitivity experiments for vertical resolution suggest that O(100,200 m) intervals are adequate to represent well-mixed boundary layers with the new scheme. The new scheme performs well at relatively low horizontal resolution (about 150 km), although inversion layers near the coast become more intense at a higher horizontal resolution (about 50 km). Copyright © 2010 Royal Meteorological Society [source]