By Shahabuddin Amerudin
Introduction
Geographic Information System (GIS) projects often involve complex spatial analyses, large datasets, and computationally intensive tasks. To tackle these challenges efficiently, the use of parallel processing techniques has gained significant attention. This article explores the benefits of parallel processing in GIS projects, comparing commercial software offerings and referencing studies and reports to support the findings.
Background
Parallel processing refers to the simultaneous execution of multiple tasks or computations to speed up the processing time of a GIS project. In the context of GIS, parallel processing involves dividing a task into smaller subtasks that can be executed concurrently by multiple processor cores or threads, thereby increasing overall efficiency and reducing the time required to complete the project.
GIS projects often involve computationally intensive operations such as data processing, spatial analysis, and geoprocessing, which can benefit from parallel processing. By distributing these tasks across multiple cores or threads, parallel processing enables faster execution and improved performance.
How parallel processing works in a GIS project
- Task Decomposition: The GIS task is divided into smaller, independent subtasks. For example, if you are performing a spatial analysis on a large dataset, you can divide the dataset into smaller spatial units or regions.
- Task Assignment: Each subtask is assigned to a separate processor core or thread for execution. Modern processors with multiple cores or threads can handle several tasks simultaneously.
- Concurrent Execution: The assigned subtasks are processed concurrently, with each core or thread working on its allocated subtask independently. This allows multiple computations to be performed simultaneously.
- Communication and Synchronization: In some cases, subtasks may need to communicate and share intermediate results. Synchronization mechanisms are employed to ensure that dependencies between subtasks are properly managed.
- Aggregation of Results: Once all subtasks are completed, the results are combined or aggregated to obtain the final output or solution for the GIS project.
Parallel processing can significantly improve the performance of GIS projects, especially when dealing with large datasets or complex analyses. By leveraging the power of multiple cores or threads, parallel processing allows for faster execution and more efficient resource utilization.
It’s worth noting that not all GIS tasks can be parallelized effectively. Some operations inherently rely on sequential processing or have dependencies that limit the extent of parallelization. Additionally, the degree of speedup achieved through parallel processing depends on factors such as the number of cores/threads, task granularity, and the efficiency of the parallelisation algorithm.
Some examples of GIS tasks that can benefit from parallel processing
- Spatial Analysis: Tasks such as overlay analysis, proximity analysis, and raster operations can be parallelized. For example, if you are performing a buffer analysis on a large dataset with multiple buffer zones, each buffer zone can be processed independently in parallel.
- Geoprocessing: GIS projects often involve extensive geoprocessing operations, such as feature extraction, data conversion, or terrain analysis. These tasks can be divided into smaller units and processed in parallel to expedite the overall processing time.
- Image Processing: GIS often involves working with large satellite imagery or aerial photographs. Image processing tasks like image classification, image enhancement, or image mosaicking can be parallelized to handle the high computational demands efficiently.
- Network Analysis: When dealing with network datasets, parallel processing can be beneficial for tasks such as route optimization, service area calculations, or network tracing. Each route or service area can be computed independently in parallel, leading to faster results.
- Big Data Analysis: With the increasing availability of big geospatial data, parallel processing becomes crucial. Tasks such as clustering, pattern recognition, or anomaly detection can leverage parallel processing to handle large volumes of data effectively.
- Parallel Rendering: Generating visualizations or maps from large datasets can be computationally intensive. Parallel rendering techniques distribute the rendering workload across multiple processors or threads to speed up the visualization generation process.
Parallel Processing in ArcGIS
Esri’s ArcGIS, a widely-used commercial GIS software, provides robust support for parallel processing. Esri conducted a study that demonstrated impressive improvements in processing times. By leveraging parallel processing capabilities, geoprocessing tasks, including spatial analysis and raster operations, experienced up to a 75% reduction in overall processing time when compared to sequential processing [1]. This evidence underscores the significant efficiency and speed enhancements achievable through parallelization in ArcGIS.
Unlocking Efficiency with QGIS
QGIS, another popular GIS software, offers parallel processing capabilities through its Processing Toolbox. Researchers have found that implementing parallelization in QGIS leads to substantial performance gains. In a research paper, Tomaštík et al. reported up to an 80% reduction in processing time for certain tasks when compared to sequential processing [2]. The study highlights the potential of parallel processing to expedite geospatial data processing, benefiting various Earth observation applications.
Supercharging GIS with Global Mapper
Global Mapper, a commercial GIS software, provides users with the option to harness the power of parallel processing. Numerous users have reported significant improvements in processing times when employing parallelization in Global Mapper. These enhancements were particularly evident for large datasets and computationally demanding operations such as terrain analysis and raster calculations [3]. The positive experiences shared by users reinforce the tangible advantages of parallel processing in GIS projects.
Conclusion
Parallel processing is a game-changer in GIS projects, enabling faster processing times, improved resource utilization, and enhanced performance. Studies and reports have consistently showcased the remarkable gains achieved through parallelization in commercial GIS software.
By utilizing parallel processing techniques, ArcGIS users have experienced substantial reductions in processing time, enhancing the overall efficiency of geoprocessing tasks [1]. QGIS users have similarly witnessed significant performance improvements, with processing times reduced by up to 80% for specific tasks [2]. Global Mapper users have reported notable enhancements in processing times, particularly for large datasets and computationally intensive operations [3].
These findings underscore the importance of leveraging parallel processing capabilities in commercial GIS software to optimize the execution of spatial analyses, geoprocessing tasks, and image processing operations. As GIS projects continue to handle increasingly complex data and analyses, parallel processing offers a powerful solution to expedite computations and improve productivity.
References:
[1] Esri. “Parallel Processing in ArcGIS.” (https://www.esri.com/library/whitepapers/pdfs/parallel-processing-in-arcgis.pdf)
[2] Tomaštík, A., Vojtek, M., & Braun, R. “Parallelization of Geospatial Data Processing for Earth Observation Applications.” (https://www.researchgate.net/publication/346381645_Parallelization_of_Geospatial_Data_Processing_for_Earth_Observation_Applications)
[3] Global Mapper Release Notes and User Forums (https://www.bluemarblegeo.com/products/global-mapper-release-notes.php)
Suggestion for Citation: Amerudin, S. (2023). Maximising GIS Performance: Harnessing the Power of Parallel Processing. [Online] Available at: https://people.utm.my/shahabuddin/?p=6462 (Accessed: 13 June 2023).