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Balancing simulation performance and computational intensity of CA models for large-scale land-use change simulations

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ENVIRONMENTAL MODELLING & SOFTWARE 2025年 185卷

作者： Liang, Zhewei Liang, Xun Jiang, Xintong Li, Tingyu Guan, Qingfeng China Univ Geosci Sch Geog & Informat Engn Wuhan 430078 Hubei Peoples R China China Univ Geosci Natl Engn Res Ctr GIS Wuhan 430078 Hubei Peoples R China China Univ Geosci Sch Future Technol Wuhan 430078 Hubei Peoples R China

Large-scale land-use change simulations are crucial for understanding land dynamics, investigating climate change, and shaping policy regulations. However, conducting fine-resolution land-use change simulations on a large scale is challenging due to high computational demands. Conversely, land-use change simulations with coarse-resolution data distort spatial details, thereby reducing simulation performance. Parallel computing can reduce computational demands but requires significant computational resources. Mixed-cell CA models offer a solution to balance simulation performance and computational intensity. The comparison experiments using various resolution land use datasets demonstrate that mixed-cell CA models, even those with coarse-resolution data, achieve results comparable to those of pure-cell CA models using fine-resolution data, but with significantly reduced simulation time. This highlights the efficiency of mixed-cell CA models in achieving comparable performance with lower computational intensity. Additionally, this study provides a measurement method for the uncertainty of mixed-cell CA models. In summary, this study reveals the unique advantages of mixed-cell CA models in efficient large-scale land use simulations, thereby providing valuable insights and guidance for future land use management and policy decisions.

关键词： Large-scale land-use simulations Mixed-cell CA models Simulation performance computational intensity

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Efficient distributed matrix for resolving computational intensity in remote sensing

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 166卷

作者： Zou, Weitao Li, Wei Wang, Zeyu Pei, Jiaming Lou, Tongtong Chen, Guangsheng Jing, Weipeng Zomaya, Albert Y. Northeast Forestry Univ Coll Comp & Control Engn 26 Hexing Rd Harbin 150040 Peoples R China Univ Sydney Ctr Distributed & High Performance Comp Sch Comp Sci Sydney NSW 2006 Australia

Remote sensing analysis is a dominant yet time-consuming part of geospatial applications. The performance can be optimized based on distributed computing, but current systems still face significant challenges. Firstly, the spatial characteristics of remote sensing data lead to an uneven distribution of computational intensity (CIT), which characterizes computing loads, including computation and IO, indifferent spatial domains. Secondly, it is hard to achieve load-balancing without introducing new computational costs, thus increasing the CIT and reducing the overall performance. Therefore, resolving CIT by decreasing and balancing it is an important research issue for distributed remote sensing computing. This paper proposes LBM-RS, an efficient distributed framework based on load-balancing matrix computing for remote sensing. It implements remote sensing applications based on the distributed matrix, representing the algorithms with a matrix computation and constructing multi-dimensional spatial domains to model computational costs for matrix operation tasks. It resolves the CIT with the minimum computation load and dynamic spatial domain decomposition strategy to support global load balancing. We also exploit the IO efficiency from the task staging strategy and the cache-aware memory structure for remote sensing data. In this way, it can reduce the bandwidth burden and memory access frequency, thus decreasing the overall CIT. Finally, we evaluate the proposed approach on both real and synthetic datasets, and the results demonstrate significant advantages in computation and communication efficiency compared to the benchmarks.

关键词： Remote sensing data computational intensity Load balancing Distributed framework Distributed matrix

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computational intensity Prediction Model of Vector Data Overlay with Random Forest Method 3rd

Computational Intensity Prediction Model of Vector Data Over...

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3rd International Conference of Pioneering Computer Scientists, Engineers and Educators (ICPCSEE)

作者： Wang, Qian Cao, Han Guo, Yan-Hui Shaanxi Normal Univ Sch Comp Sci Xian 710119 Shaanxi Peoples R China Shandong Womens Univ Jinan 250300 Shandong Peoples R China

ISBN: (纸本)9789811063855;9789811063848

Spatial analysis is the core of geographic information system (GIS), of which, spatial overlay of vector data is a major job. computational intensity of the spatial overlay has a direct effect on the overall performance of the GIS. High precision modeling for the computational intensity and analysis of the vector data overlay has been a challenging task. Thus, the paper proposes a novel approach, which utilizes self-learning and self-training features of optimized random forest algorithm to the vector data overlay analysis. Simulation experiments show that the proposed model is superior to non-optimized random forest algorithm and support vector machine model with higher prediction precision and is also capable of eliminate redundant computational intensity features.

关键词： Random forest Space analysis Vector data computational intensity Machine learning

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Formalizing computational intensity of big traffic data understanding and analysis for parallel computing

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NEUROCOMPUTING 2015年 169卷 158-168页

作者： Xia, Yingjie Chen, Jinlong Wang, Chunhui Zhejiang Univ Coll Comp Sci Hangzhou 310012 Zhejiang Peoples R China Hangzhou Normal Univ Hangzhou Inst Serv Engn Hangzhou 310012 Zhejiang Peoples R China Soochow Univ Prov Key Lab Comp Informat Proc Technol Suzhou 215006 Peoples R China

Nowadays, traffic data can be collected from different types of senors widely-deployed in urban districts. Big traffic data understanding and analysis in intelligent transportation systems (ITS) turns out to be an urgent requirement. This requirement leads to the computation-intensive and data-intensive problems in ITS, which can be innovatively resolved by using Cyber-Infrastructure (CI). A generic process for the solution contains four steps: (1) formalized data understanding and representation, (2) computational intensity transformation, (3) computing tasks creation, and (4) Cl resources allocation. In this paper, we firstly propose a computational domain theory to formally represent heterogeneous big traffic data based on the data understanding, and then use data-centric and operation-centric transformation functions to evaluate the computational intensity of traffic data analysis in different aspects. Afterwards, the computational intensity is leveraged to decompose the domain into sub-domains by octree structure. All the sub-domains create computing tasks which are scheduled to CI resources for parallel computing. Based on the evaluation of overall computational intensity, an example of fusing Sydney Coordinated Adaptive Traffic System (SCATS) data and Global Positioning System (GPS) data for traffic state estimation is parallelized and executed on Cl resources to test the accuracy of domain decomposition and the efficiency of parallelized implementation. The experimental results show that the ITS computational domain is decomposed into load-balanced sub-domains, therefore facilitating significant acceleration for parallelized big traffic data fusion. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Intelligent transportation systems computational intensity computational domain Formalization Parallel computing

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computational intensity Prediction Model of Vector Data Overlay with Random Forest Method

Computational Intensity Prediction Model of Vector Data Over...

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The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Qian Wang Han Cao Yan-Hui Guo School of Computer Science Shaanxi Normal University Shandong Women's University

Spatial analysis is the core of geographic information system(GIS),of which, spatial overlay of vector data is a major job. computational intensity of the spatial overlay has a direct effect on the overall performance of the *** precision modeling for the computational intensity and analysis of the vector data overlay has been a challenging task. Thus, the paper proposes a novel approach, which utilizes self-learning and self-training features of optimized random forest algorithm to the vector data overlay analysis. Simulation experiments show that the proposed model is superior to non-optimized random forest algorithm and support vector machine model with higher prediction precision and is also capable of eliminate redundant computational intensity features.

关键词： Random forest Space analysis Vector data computational intensity Machine learning

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A CRAY-2 VERSUS CM-2-COMPARISON USING SEVERAL POLYNOMIAL BENCHMARKS

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PARALLEL COMPUTING 1992年第8期18卷 931-945页

作者： BUTEL, R Laboratoire de Météorologie Dynamique du C.N.R.S. 24 rue Lhomond 75231 Paris Cedex 05 France

In the past benchmarkings were done with sets of various program kernels. Some recent developments showed that one can gain more information using a regular family of kernels, in particular to characterize the response of a given computer to the computational intensity (the ratio between computational load and transfers from and to memory). In this note we use four general fast regular families of kernels to benchmark a Cray-2 computer and a CM-2 computer. We are very interested in the impact of the compilers' behaviors on benchmarking. We propose a new way of displaying results of benchmarking, which helps to point out compilers' oddness. Finally we emphasize the impact of n1/2 (half performance vector length) on real efficiency of supercomputers.

关键词： COMPUTER TESTING PARALLEL PROCESSING ASYMPTOTIC PERFORMANCE BEHAVIOR computational intensity AUTOTASKING SPEEDUP PARALLEL PROGRAMMING CRAY-2 CONNECTION MACHINE-2

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computational forces in the Linpack benchmark

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2008年第9期68卷 1283-1290页

作者： Numrich, Robert W. Univ Minnesota Minnesota Supercomp Inst Minneapolis MN 55455 USA

Dimensional analysis reduces a complicated ten-parameter formula for the execution time of the Linpack benchmark to a simpler two-parameter formula. These two parameters are ratios of software forces and hardware forces that determine a self-similarity Surface. Machines move along paths on this surface as the problem size and the number of processors change. Two machines scale the same way, they move along the same path, if they have the same hardware forces. To design efficient algorithms, the programmer must produce software forces large enough to overcome the hardware forces. Modern machines have larger hardware forces than older machines and are harder to program. (C) 2008 Elsevier Inc. All rights reserved.

关键词： scalability dimensional analysis performance analysis computational intensity computational force parallel numerical algorithms Linpack benchmark

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PARALLEL COMPUTING 2011年第2期37卷 69-84页

作者： Numrich, Robert W. Heroux, Michael A. Univ Minnesota Minnesota Supercomp Inst Minneapolis MN 55455 USA Sandia Natl Labs Albuquerque NM 87185 USA St Johns Univ Collegeville MN 56321 USA

Self-similarity is a property of physical systems that describes how to scale parameters such that dissimilar systems appear to be similar. Computer systems are self-similar if certain ratios of computational forces, also known as computational intensities, are equal. Two machines with different computational power, different network bandwidth and different inter-processor latency behave the same way if they have the same ratios of forces. For the parallel conjugate gradient algorithm studied in this paper, two machines are self-similar if and only if the ratio of one force describing latency effects to another force describing bandwidth effects is the same for both machines. For the two machines studied in this paper, this ratio, which we call the mixing coefficient, is invariant as problem size and processor count change. The two machines have the same mixing coefficient and belong to the same equivalence class. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Parallel algorithms Benchmark analysis computational intensity computational force Dimensional analysis Equivalence class Self-similarity Scaling Mixing coefficient

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A Generic Methodological Framework for Cyber-ITS: Using Cyber-infrastructure in ITS Data Analysis Cases

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FUNDAMENTA INFORMATICAE 2014年第1期133卷 35-53页

作者： Xia, Yingjie Zhang, Ting Wang, Shengbao Hangzhou Normal Univ Hangzhou Inst Serv Engn Hangzhou 310012 Zhejiang Peoples R China

This paper presents a method to capture the computational intensity and computing resource requirements of data analysis in intelligent transportation systems (ITS). These requirements can be transformed into a generic methodological framework for Cyber-ITS, mainly consisting of region-based ITS data divisions and tasks scheduling for processing, to support the efficient use of cyber-infrastructure (CI). To characterize the computational intensity of a particular ITS data analysis, the computational transformation is performed by data-centric and operation-centric transformation functions. The application of this framework is illustrated by two ITS data analysis cases: multi-sensor data fusion for traffic state estimation by integrating rough set theory with Dempster-Shafer (D-S) evidence theory, and geospatial computation on Global Positioning System (GPS) data for advertising value evaluation. To make the design of generic parallel computing solutions feasible for ITS data analysis in these cases, an approach is developed to decouple the region-based division from specific high performance computer (HPC) architecture and implement a prototype of the methodological framework. Experimental results show that the prototype implementation of the framework can be applied to divide the ITS data analysis into a load-balanced set of computing tasks, therefore facilitating the parallelized data fusion and geospatial computation to achieve remarkable speedup in computation time and throughput, without loss in accuracy.

关键词： Intelligent Transportation Systems Cyber-infrastructure computational intensity Framework

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computational forces in the SAGE benchmark

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2009年第3期69卷 315-325页

作者： Numrich, Robert W. Univ Minnesota Minnesota Supercomp Inst Minneapolis MN 55455 USA

Dimensional analysis applied to a complicated timing formula for the SAGE benchmark yields new insight into the limits to scalability. A single surface, defined by two curvilinear coordinates, describes the parallel efficiency of the benchmark. Each machine, as a function of the number of processors, follows its own path on the surface determined by dimensionless ratios of hardware forces to software forces. Two machines with the same ratios follow the same path and are self-similar, even though the numerical value of each individual force may be different. For this benchmark, latency effects are unimportant relative to bandwidth effects because of the slab decomposition used to distribute the problem across processors. To a good first-order approximation, a single force ratio describes the efficiency as a function of the number of processors. A simpler model, with a single dimensionless exponent, describes the first-order behavior of the computational power as a function of the number of processors. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Parallel algorithms Benchmark analysis computational intensity computational force Dimensional analysis Self-similarity Scaling

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