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mcRPL: a general purpose parallel raster processing library on distributed heterogeneous architectures.

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Title: mcRPL: a general purpose parallel raster processing library on distributed heterogeneous architectures.
Authors: Gao, Huan1,2 (AUTHOR), Peng, Xuantong3 (AUTHOR), Guan, Qingfeng1,2 (AUTHOR), Wang, Jingyi4 (AUTHOR), Liu, Ziqi1,2 (AUTHOR), Yang, Xue1,2 (AUTHOR), Zeng, Wen1,2 (AUTHOR)
Source: International Journal of Geographical Information Science. Sep2023, Vol. 37 Issue 9, p2043-2066. 24p.
Subjects: Distributed computing, Library technical services, Parallel processing, Heterogeneous distributed computing, Heterogeneous computing, Parallel algorithms
Abstract: Parallel computing on distributed heterogeneous architectures (e.g. computing clusters with multiple CPUs and GPUs) can significantly improve the computational efficiency and scalability of complicated algorithms, but it is theoretically and technically complex. Parallel raster processing libraries reduce the development complexity of parallel raster algorithms by hiding parallel computing details; however, no existing library sufficiently utilizes distributed heterogeneous computing resources. A general-purpose raster processing library (mcRPL) combining multi-process parallelism and multi-thread parallelism is proposed to enable parallel raster processing on distributed heterogeneous architectures with multiple CPUs and GPUs. Additionally, an adaptive hardware assignment strategy is proposed to fully utilize available processors in various hardware environments. A series of task-processing strategies are adopted to aim toward maximizing the utilization of the computing capacity of involved processors. Experiments revealed that two raster algorithms parallelized using mcRPL for spatiotemporal data fusion and land-use change simulation were 170.7- and 143.2-fold faster than original serial algorithms using 8 and 16 GPUs, respectively. While hiding the details of mixed parallelism and reducing the development complexity, mcRPL provides user-friendly interfaces for the development of parallel raster algorithms to enhance computational performance and enable large-scale raster computing tasks with extensive data volumes. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: mcRPL: a general purpose parallel raster processing library on distributed heterogeneous architectures.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geographical+Information+Science%22">International Journal of Geographical Information Science</searchLink>. Sep2023, Vol. 37 Issue 9, p2043-2066. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink><br /><searchLink fieldCode="DE" term="%22Library+technical+services%22">Library technical services</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+processing%22">Parallel processing</searchLink><br /><searchLink fieldCode="DE" term="%22Heterogeneous+distributed+computing%22">Heterogeneous distributed computing</searchLink><br /><searchLink fieldCode="DE" term="%22Heterogeneous+computing%22">Heterogeneous computing</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+algorithms%22">Parallel algorithms</searchLink>
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  Data: Parallel computing on distributed heterogeneous architectures (e.g. computing clusters with multiple CPUs and GPUs) can significantly improve the computational efficiency and scalability of complicated algorithms, but it is theoretically and technically complex. Parallel raster processing libraries reduce the development complexity of parallel raster algorithms by hiding parallel computing details; however, no existing library sufficiently utilizes distributed heterogeneous computing resources. A general-purpose raster processing library (mcRPL) combining multi-process parallelism and multi-thread parallelism is proposed to enable parallel raster processing on distributed heterogeneous architectures with multiple CPUs and GPUs. Additionally, an adaptive hardware assignment strategy is proposed to fully utilize available processors in various hardware environments. A series of task-processing strategies are adopted to aim toward maximizing the utilization of the computing capacity of involved processors. Experiments revealed that two raster algorithms parallelized using mcRPL for spatiotemporal data fusion and land-use change simulation were 170.7- and 143.2-fold faster than original serial algorithms using 8 and 16 GPUs, respectively. While hiding the details of mixed parallelism and reducing the development complexity, mcRPL provides user-friendly interfaces for the development of parallel raster algorithms to enhance computational performance and enable large-scale raster computing tasks with extensive data volumes. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/13658816.2023.2244550
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        Text: English
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        Type: general
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      – SubjectFull: Parallel processing
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      – TitleFull: mcRPL: a general purpose parallel raster processing library on distributed heterogeneous architectures.
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              Text: Sep2023
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              Y: 2023
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