Geocomputation over the Emerging Heterogeneous Computing Infrastructure

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Abstract
Emerging computer architectures and systems that combine multi‐core CPUs and accelerator technologies, like many‐core Graphic Processing Units (GPUs) and Intel's Many Integrated Core (MIC) coprocessors, would provide substantial computing power for many time‐consuming spatial‐temporal computation and applications. Although a distributed computing environment is suitable for large‐scale geospatial computation, emerging advanced computing infrastructure remains unexplored in GIScience applications. This article introduces three categories of geospatial applications by effectively exploiting clusters of CPUs, GPUs and MICs for comparative analysis. Within these three benchmark tests, the GPU clusters exemplify advantages in the use case of embarrassingly parallelism. For spatial computation that has light communication between the computing nodes, GPU clusters present a similar performance to that of the MIC clusters when large data is applied. For applications that have intensive data communication between the computing nodes, MIC clusters could display better performance than GPU clusters. This conclusion will be beneficial to the future endeavors of the GIScience community to deploy the emerging heterogeneous computing infrastructure efficiently to achieve high or better performance spatial computation over big data.
Funding Information
  • National Science Foundation (OCI-1047916)

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