Parallel processing algorithms for gis guide books. Whilst todays processors are adequate for the majority of gis uses, some applications are too processorintensive to be deemed. Parallel algorithms the parallel algorithms usually divide the problem into more symmetrical or asymmetrical subproblems and pass them to many processors and put the results back together at one end. You can check which tools honor the parallel processing setting by going to the help. Focus will be on the interplay between theory and practice and scalability to large data. Parallel algorithms are highly useful in processing huge volumes of data in quick time. In this paper, we comparatively investigate the performance of parallel radial basis function rbfbased, moving least square mlsbased, and shepards interpolation algorithms for building dems by evaluating the influence of terrain type, raw data density, and distribution patterns on the interpolation accuracy and computational efficiency.
Applied sciences free fulltext an efficient parallel algorithm for. For example, organizations may have a dedicated server used just to build map caches. International journal of geographical information science a. An efficient parallel algorithm for polygons overlay analysis mdpi. Abstract the availability of parallel processing computers based on. Desa regional research laboratory, scotland department of geography university of edinburgh drummond street.
Pdf mapreduce algorithms for gis polygonal overlay processing. As a consequence, our understanding of parallel algorithms has increased remarkably over the past ten years. These algorithms are well suited to todays computers, which basically perform operations in a sequential fashion. Healeys architecture adopted layered idea, each layer only focusing on their own functions, without. Parallel processing algorithms for gis healey, richard on. Desa regional research laboratory, scotland department of geography university of edinburgh drummond street edinburgh eh8 9xp scotland, u. Distributed frameworks and parallel algorithms for processing largescale geographic data. Distributed frameworks and parallel algorithms for processing largescale geographic data kenneth a. Class overview this class explores fundamental gis applications and the algorithms and data structures involved.
We also identify key problems and future potential research directions of parallel gis. Note that we export the figure into a pdf, which is converted into the eps format for publication separately because transparency in eps is not natively supported in matplotlib. A parallel algorithm can be executed simultaneously on many different processing devices and then combined together to get the correct result. I usually find it useful to use this setting for building caches. Furthermore, the paper gives some examples from simple and advanced gis and spatial data. Single instruction stream multiple data stream simd a single stream of instructions is broadcasted to a number of processors. Different choices of interpolation algorithms may trigger significant differences in interpolation accuracy and computational efficiency, and a proper interpolation algorithm needs to be carefully used based on the specific. Pdf efficient parallel and distributed algorithms for gis. Parallel online spatial and temporal aggregations on multi. Parallel buffer generation algorithm for gis longdom publishing sl. Parallel algorithms could now be designed to run on special purpose parallel.
Prasad algorithms, computer science, cuda, electronic design. As more computers have incorporated some form of parallelism, the emphasis in algorithm design has shifted from sequential algorithms to parallel algorithms, i. Parallel algorithms for constructing range and nearest. Efficient parallel and distributed algorithms for gis polygon. Over the last fifteen years gis has become a fullyfledged technology, deployed across a range of application areas. Our research vision is to deliver similar results for other common data processing operations and in nontrivial topologies. Gis processing on desktop computers are quite insufficient in terms of performance. In computer science, a parallel algorithm, as opposed to a traditional serial algorithm, is an algorithm which can do multiple operations in a given time. Polygon overlay is one of the complex operations in geographic. This class explores fundamental gis applications and the algorithms and data structures involved.
Parallel computer has p times as much ram so higher fraction of program memory in ram instead of disk an important reason for using parallel computers parallel computer is solving slightly different, easier problem, or providing slightly different answer in developing parallel program a better algorithm. Increasingly, parallel processing is being seen as the only costeffective method for the fast solution of computationally large and dataintensive problems. Parallel processing algorithms pdf parallel processing algorithms pdf. Spreading a geoprocessing operation across multiple processes can speed up performance. These algorithms are well suited to todays computers, which basically perform operations in a. This text focuses on the ways in which technology can be applied to gis applications, emphasizing software. Yet in practice, a greedy topologyaware heuristic performs aggregation up to 3.
It is used in geographic information systems gis, computer graphics, and vlsi cad. By default, the command will attempt to employ parallel processing, using up to 3 cores simultaneously. Parallel processing strategies for big geospatial data. Distributed frameworks and parallel algorithms for. As the size of data in gis keeps growing, the design of ef. We identify three thrusts in realizing this vision. Parallelizing multiple flow accumulation algorithm using. Parallel processing algorithms for gis in searchworks catalog. Spatial data decomposition is the basis of parallel computing architecture. Knoblock, university of southern california maps depict natural and humaninduced changes on earth at.
This tutorial provides an introduction to the design and analysis of parallel algorithms. The application of parallel processing to computationally intensive gis problems has been advocated and illustrated by many researchers over the last twenty years. Parallel processing technologies have become omnipresent in the majority of new proces. Wiley series on parallel and distributed computing. Distributed frameworks and parallel algorithms for processing large. Spatial data decomposition is the basis of parallel computing architecture based on the spatial data. We use the parallel processing of grass as a case study. A number of parallel algorithms were uniformly and efficiently developed, thus certifying the validity of the multi.
The s flag will disable parallel processing, but does use an optimized r. The upper gis library fully realizes parallel gis algorithms library. Efficient parallel and distributed algorithms for gis polygonal overlay processing. James a a computer science division, school of informatics, university of wales, bangor. It has been a tradition of computer science to describe serial algorithms in abstract machine models, often the one known as randomaccess machine. Geographical information system parallelization for.
Parallel processing algorithms for gis crc press book. Distributed frameworks and parallel algorithms for processing. Based on the model and methods, we propose an open. Pul, which isthe core part of parallel gis, mainly provides a uni. Spatial data decomposition is the basis of parallel computing. Parallel online spatial and temporal aggregations on multicore cpus and manycore gpus jianting zhang, department of computer science, the city college of new york, new york. There after all these stages of the pipeline are kept busy until the final components and enter the pipe. Our experiment results show that we are able to spatially associate nearly 170 million. Efficient parallel and distributed algorithms for gis. Still under active developments, cudagis currently supports major types of geospatial data point, polyline, polygon. Richard healey, steve dowers, bruce gittings, mike j mineter. Nov 26, 2015 then we summarize the current spatial data partition strategies, key methods to realize parallel gis in the view of data decomposition and progress of the special parallel gis algorithms. Pdf efficient parallel and distributed algorithms for gis polygonal.
Distributed parallel processing in a cloud environment provides an efficient solution to. The parallel gis algorithm is an efficient way to conduct map overlay analysis 4 5 67. New parallel algorithm and system prototype satish puri, sushil k. Geographic information science and technology gist is enjoying profound innovation in its research and development. Tools that honor the parallel processing factor environment will divide and perform operations across multiple processes. The three pansharpened output channels may be combined with d. The parallel gis algorithm is an efficient way to conduct map overlay. Parallel processing is a technology now coming of age in a diversity of application domains, notably gis where large data sets are involved. Describe and characterize the performance of parallel algorithms in the literature and. We have focussed on gis as an example application domain since it provides very large data sets that can benefit from parallel and distributed computing. Efficient parallel and distributed algorithms for gis polygonal. Algorithms in which several operations may be executed simultaneously are referred to as parallel algorithms.
This has not been for the lack of individual parallel algorithms, but. Numeric weather prediction nwp uses mathematical models of atmosphere and oceans taking current observations of weather and processing these data with computer models to forecast the future state of weather. Algorithms and architectures, plenum, new york, 1999. Geographical information system parallelization for spatial. Mapreduce algorithms for gis polygonal overlay processing. This is a draft of a paper that will appear in acms computing surveys in the 50thaniversary issue, and is a condensed version of a chapter that will appear in the crc handbook on computer science. A high performance spatial data warehousing system over mapreduce ablimit aji1 fusheng wang2 hoang vo1 rubao lee3 qiaoling liu1 xiaodong zhang3 joel saltz2 1department of mathematics and computer science, emory university. We report the design and realization of a highperformance parallel gis, i. This scheme, in which all processors execute the same program, is called a single instruction stream, multiple data stream simd. The parallel gis algorithm is an efficient way to conduct map overlay analysis 4 7. Parallel algorithms for constructing range and nearestneighbor searching data structures pankaj k. Nowadays, just about any application that runs on a computer will encounter the parallel processors now available in almost every system.
These highly visual and highly applied texts will promote. Comparative investigation of parallel spatial interpolation. Free the design and analysis of parallel algorithms pdf download this text for students and professionals in computer science provides a valuable overview of current knowledge concerning parallel algorithms these computer operations have recently acquired increased. James a a computer science division, school of informatics, university of wales, bangor, north wales ll57 1ut, uk b department of computer science, university of adelaide, sa 5005, australia received august 2002. Most of todays algorithms are sequential, that is, they specify a sequence of steps in which each step consists of a single operation. Explore selected gis applications and the algorithmsdata structures involved. This paper provides an abstract analysis of parallel processing strategies for. Then we summarize the current spatial data partition strategies, key methods to realize parallel gis in the view of data decomposition and progress of the special parallel gis algorithms. Despite this, gis users have been slow to capitalize on the potential which the technology offers. Download the design and analysis of parallel algorithms pdf summary.
Parallel processing algorithms for gis 1st edition. Geographical information system parallelization for spatial big. Spreading a geoprocessing operation across multiple processes can speed up performance by taking advantage of more than one core. Parallel processing algorithms pdf overview of some serial algorithms. The subject of this chapter is the design and analysis of parallel algorithms. Introduction to parallel computing, second edition. Prasad, phd abstract polygon clipping is one of the complex operations in computational geometry. Pdf efficient parallel and distributed algorithms for.
The resource consumption in parallel algorithms is both processor cycles on each processor and also the communication overhead between the processors. A high performance spatial data warehousing system over mapreduce ablimit aji1 fusheng wang2 hoang vo1 rubao lee3 qiaoling liu1 xiaodong zhang3 joel saltz2 1department of mathematics and computer science, emory university 2department of biomedical informatics, emory university 3department of computer science and engineering, the ohio state university. The parallel gis algorithms implemented on clusters of multicore sharedmemory computers, based on frameworks such as mpi message passing interface, mapreducehadoop, and apache spark, have set this paradigm as an emerging research and development topic 3. The parallel gis algorithm is an efficient way to conduct map overlay analysis 4,5,6,7.
Parallel processing divides a large task into many. International journal of geographical information science. This tutorial provides an introduction to the design and analysis of. Parallelizing multiple flow accumulation algorithm using cuda. Sage advances in gist will provide students with learning resources to support and enrich their interest in the workings of geographic information science and technology. Given that modern computers are mostly multicore or have multiple cpus, parallel processing becomes a very important solution for speedup. Similarly, many computer science researchers have used a socalled parallel randomaccess.
G43 2011 00435dc22 2010043659 printed in the united. One way to build a cache is using the manage tile cache tool, which honors parallel processing. The building of largescale digital elevation models dems using various interpolation algorithms is one of the key issues in geographic information science. Efficient parallel and distributed algorithms for gis polygon overlay processing by satish puri under the direction of sushil k. Fundamentals of parallel processing 215 stage 1 stage 2 stage 3 a i b i a i1 b i1 a i2 b i2 fig.
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