Load imbalance aspects in atmosphere simulations

Koziar, Christian; Reilein, Robert; Rünger, Gudula

doi:10.1109/icppw.2001.951916

Cited by 3 publications

(1 citation statement)

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“…These simulations include numerical weather models with appropriate boundary conditions (14,37), where the physical space is usually decoupled in regular grid cells and the numerical computations on inner grid cells are performed uniformly (38). The computational cost of the models is a function of the number of cells, the time step, and algorithms used for the domain (39).…”

Section: High-performance Spatial Computing For Enabling Dust-storm Rmentioning

confidence: 99%

Using spatial principles to optimize distributed computing for enabling the physical science discoveries

Yang

Huang

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

108

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Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century. G lobalization over the past decades has raised human awareness of global challenges, such as global warming, severe weather conditions and rapid diffusion of contagious diseases (1, 2). These challenges require advancement in the physical sciences to better understand the world around us. Spatial data, the information related to space and time, are critical to scientific advancement by providing multidimensional (3D space, 1D time, and important parameters) records of the Earth. Large-scale data are typically collected, generated, and stored in geographically dispersed locations and must therefore be supported by distributed computing facilities. Seamless sharing and access to these resources calls for a spatial cyberinfrastructure (CI) to enable the use of spatial data for the advancement of the physical sciences (1, 3, 4). Global initiatives, such as the Global Earth Observation System of Systems (GEOSS, an initiative to integrate Earth observation data to address regional to global problems such as climate change), Digital Earth (an initiative to integrate Earth referenced data to provide intuitive and better end-user tools), and the Spatial Data Infrastructure (SDI, an initiative to build an infrastructure to seamlessly share spatia...

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Section: High-performance Spatial Computing For Enabling Dust-storm Rmentioning

confidence: 99%