As an important spatiotemporal simulation approach and an effective tool for developing and examining spatial optimization strategies (e.g., land allocation and planning), geospatial cellular automata (CA) models often require multiple data layers and consist of complicated algorithms in order to deal with the complex dynamic processes of interest and the intricate relationships and interactions between the processes and their driving factors. Also, massive amount of data may be used in CA simulations as high-resolution geospatial and non-spatial data are widely available. Thus, geospatial CA models can be both computationally intensive and data intensive, demanding extensive length of computing time and vast memory space. Based on a hybrid parallelism that combines processes with discrete memory and threads with global memory, we developed a parallel geospatial CA model for urban growth simulation over the heterogeneous computer architecture composed of multiple central processing units (CPUs) and graphics processing units (GPUs). Experiments with the datasets of California showed that the overall computing time for a 50-year simulation dropped from 13,647 seconds on a single CPU to 32 seconds using 64 GPU/CPU nodes. We conclude that the hybrid parallelism of geospatial CA over the emerging heterogeneous computer architectures provides scalable solutions to enabling complex simulations and optimizations with massive amount of data that were previously infeasible, sometimes impossible, using individual computing approaches.
Image segmentation is a very important step in many GIS applications. Mean shift is an advanced and versatile technique for clusteringbased segmentation, and is favored in many cases because it is nonparametric. However, mean shift is very computationally intensive compared with other simple methods such as k-means. In this work, we present a hybrid design of mean shift algorithm on a computer platform consisting of both CPUs and GPUs. By taking advantages of the massive parallelism and the advanced memory hierarchy on Nvidia's Fermi GPU, the hybrid design achieves a 20× speedup compared with the pure CPU implementation when dealing with images bigger than 1024×1024 pixels.
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.
Graphics processing units (GPUs) are capable of achieving remarkable performance improvements for a broad range of applications. However, they have not been widely adopted in embedded systems and mobile devices as accelerators mainly due to their relatively higher power consumption compared with embedded microprocessors. In this work, we conduct a comprehensive analysis regarding the feasibility and potential of accelerating applications using GPUs in low-power domains. We use two different categories of benchmarks: (1) the Level 3 BLAS subroutines, and (2) the computer vision algorithms, i.e., mean shift image segmentation and scale-invariant feature transform (SIFT). We carried out our experiments on the Nvidia CARMA development kit, which consists of a Nvidia Tegra 3 quad-core CPU and a Nvidia Quadro 1000M GPU. It is found that the GPU can deliver a remarkable performance speedup compared with the CPU while using a significantly less energy for most benchmarks. Further we propose a hybrid approach to developing applications on platform with GPU accelerators. This approach optimally distributes workload between the parallel GPU and the sequential CPU to achieve the best performance while using the least energy.
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