Results of the summarizing process for the arXiv paper: 1912.06548v1

The chapter titled "High Performance Computing for Geospatial Applications: A Retrospective View" by Marc P. Armstrong discusses the importance of high performance computing (HPC) in addressing the computational complexity of geospatial analyses. These analyses often involve solution processes that require numerous iterations or combinatorial comparisons, making them computationally challenging. The author highlights that the complexity of geospatial analyses is further exacerbated by the rapidly growing volumes of geospatial information, which can impede analysis and display. Therefore, the application of HPC becomes crucial in effectively handling these challenges. The chapter provides an overview of past implementations of HPC in geospatial applications. It mentions that many previous applications were developed using hardware systems with short life-spans and implemented in software that was not easily portable. This limited their scalability and hindered their potential for wider adoption. However, recent approaches have shown promise by leveraging distributed resources such as cyberinfrastructure, cloud computing, and fog computing. These approaches offer more flexibility and scalability compared to traditional implementations. Overall, this chapter emphasizes the need for HPC in geospatial applications due to their computational complexity and the increasing volume of geospatial data. The shift towards utilizing distributed resources presents a more promising avenue for addressing these challenges and enabling efficient analysis and display of geospatial information.

• High performance computing (HPC) is crucial for addressing the computational complexity of geospatial analyses
• Geospatial analyses often involve solution processes that require numerous iterations or combinatorial comparisons
• Rapidly growing volumes of geospatial information further complicate these analyses
• HPC helps in effectively handling these challenges
• Previous implementations of HPC in geospatial applications had limitations in scalability and portability
• Recent approaches leverage distributed resources like cyberinfrastructure, cloud computing, and fog computing for more flexibility and scalability
• The need for HPC in geospatial applications is emphasized due to computational complexity and increasing volume of geospatial data
• Utilizing distributed resources presents a promising avenue for efficient analysis and display of geospatial information.

High Performance Computing for Geospatial Applications: A Retrospective View

Geospatial analyses are often computationally complex and require numerous iterations or combinatorial comparisons to solve. This complexity is further exacerbated by the rapidly growing volumes of geospatial information, which can impede analysis and display. High performance computing (HPC) has become increasingly important in addressing these challenges. This article provides an overview of past implementations of HPC in geospatial applications and discusses recent approaches that leverage distributed resources such as cyberinfrastructure, cloud computing, and fog computing.

Past Implementations of HPC in Geospatial Applications

Many previous applications were developed using hardware systems with short life-spans and implemented in software that was not easily portable. This limited their scalability and hindered their potential for wider adoption. Additionally, some implementations relied on a single computer to process data which could be time consuming due to the large amount of data involved in geospatial analyses. Furthermore, many existing systems lacked the necessary flexibility to handle different types of datasets or algorithms used for analysis.

Recent Approaches Leveraging Distributed Resources

Recent approaches have shown promise by leveraging distributed resources such as cyberinfrastructure, cloud computing, and fog computing. These approaches offer more flexibility compared to traditional implementations since they allow users to access multiple computers simultaneously from anywhere with an internet connection thus allowing them to scale up their processing power when needed without having to invest in additional hardware or software licenses. Additionally, these distributed resources provide access to powerful computational tools that can help address the complexity associated with geospatial analyses while also providing cost savings due to shared infrastructure costs among users who may only need occasional access rather than investing heavily upfront into dedicated hardware solutions.

Conclusion

Overall, this chapter emphasizes the need for HPC in geospatial applications due to their computational complexity and the increasing volume of geospatial data available today. The shift towards utilizing distributed resources presents a more promising avenue for addressing these challenges efficiently while enabling analysis and display of complex datasets at scale without requiring significant upfront investments into dedicated hardware solutions or software licenses.