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

TLDR

Contemporary physical science studies rely on geographically dispersed spatial data and simulations, but single computers and generic high‑end computing are insufficient; distributed computing optimized by spatial principles is required to process these complex analyses, as spatial principles govern interactions across space and time. This paper demonstrates how spatial computing can leverage spatial principles to advance physical science by enabling efficient data access, high‑performance computing, and multidimensional visualization. Spatial computing is a computing paradigm within a spatial cyberinfrastructure that uses spatial principles to optimize distributed computing for physical science, illustrated through examples of efficient data access, high‑performance computing, and multidimensional visualization. The examples show that spatial computing is essential for improved data access, simulation, and visualization, and it is envisioned to become a core technology driving 21st‑century physical science advancements.

Abstract

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.