Utilization of Parallel Computer System for Modeling of Geological Phenomena in GIS

8/14/2019 Utilization of Parallel Computer System for Modeling of Geological Phenomena in GIS

1/5

METALURGIJA43 (2004) 4, 287-291 287

L. VOKOROKOS et al.: UTILIZATION O PARALLEL COMPUTER SYSTEM OR MODELING ...

Received - Primljeno: 2003-12-16Accepted - Prihvaeno: 2004-04-20

Preliminary Note - Prethodno priopenje

ISSN 0543-5846METABK 43 (4) 287-291 (2004)

UDC - UDK 551.4:004.9 GIS=111

L. VOKOROKOS, P. BLI AN, S. PETRK, N. DM

L. Vokorokos, S. Petrk, N. dm, aculty of Electrical Engineering andInformatics Technical University of Ko ice, Ko ice, Slovakia, P. Bli an,aculty of Mining, Ecology, Management and Geotechnologies Techni-

cal University of Ko ice, Slovakia

UTILIZATION O. PARALLELCOMPUTER SYSTEM .OR MODELING O. GEOLOGICAL PHENOMENA IN GIS

INTRODUCTION

Present times can be defined by growing amount of data and information as well as growing complexity of data flows. In order to process this amount of data andinformation there are efforts to development of special-ized systems to process of variety of geographical data -Geographical Information Systems (GIS). The usage of GISs has successfully spread to the area of geology, min-ing and metallurgy.

Geological phenomena modelling using GIS dependson correct interpretation of large amount of data acquired by measurement and observation. Both primary analysisand processing of acquired data are usually performed on

Geographical data processing using Geographical Information Systems on single processor computer is goingto be very difficult in consideration of constantly growing amount of data. In the recent years there is a trend of operation of those systems on parallel computers, which brings an effect mainly in the area of their performance.This article deals with possibilities of shortening of time demanded for mathematically exacting process of both

analysis and visualization of variety of geographical data using parallel architectures. It focuses on some meth-ods of geographical data processing as well as on types of parallel computing systems, together with descriptionof their practical application to the environment of Geographical Information Systems.

Key words: Geographical Information System (GIS), geographical data, parallel computer, interpolation methods

Uporaba usporednog raunalnog sustava za modeliranje geolo kih fenomena u sustavu zemljopisnihinformacija (GIS). Obrada zemljopisnih podataka uporabom Geographical Information System (GIS) na jedno-procesnom kompjutoru bit e vrlo te ka ako se uzme u obzir stalni porast koliine podataka. Zadnjih godinapostoji tendencija rada tih sustava na usporednim raunalima, a to ima utjecaja uglavnom na podruje njihovihperformansi. Ovaj lanak se bavi mogunostima skraivanja vremena potrebnog za komplicirane (exacting)matematike operacije i vizualizacije mno tva zemljopisnih podataka uporabom usporednih arhitektura.Usredotouje se na neke metode obrade zemljopisnih podataka kao i na tipove usporednih raunalnih sustava,zajedno s opisom njihove praktine primjene na okoli Grographical Information Systems.

Kljune rijei: sustav informiranja o zemljopisnim podacima (GIS), geografski podaci, usporedni kompjuteri,interpolacijske metode

single processor computer. This fact caused, that entire process is going to be highly time-consuming.

Next step in a process of computer performance increas-ing was to merge performance of two or more processors.High performance multiprocessor systems [1] encompassmathematically highly exacting visualization [2] of model-ling methods (e.g. geological objects modelling in geology).

PARALLELCOMPUTER SYSTEMS ARCHITECTURES

Multiprocessor system (MS) [1] is a group of inter-connected computers, which are either solving one com- plex task or independently contributing to the processingof couple of programs.

Speed of MS in generally depends on used intercon-nection network between processors as well as on bothsuitability of used parallel architecture and used parallel

algorithms.


2/5


3/5

METALURGIJA43 (2004) 4, 287-291 289


compendium of contributions g 1- g 9 from the known pointsG1 - G9 to B.

Hence, the value of observed variable TB mostly depends on both known values and distances of d 1 - d 9 pointsfrom B. Logically said: the contribution of G1 ( igure 2.)with known value g 1 to B will be surely less then the con-tribution of G7 with known value g 7. This dependency can by expressed by the following equation:

1

1

1

nik ii

B n

k ii

gd

T

d

=

=

=

(1)

IDS method is mathematically more difficult as thewidely used linear interpolation. It is often used for model-ling of more exacting terrain forms than usually, because of consideration of some irregularity in spatial distribution of observed phenomena. (Distribution of beneficial compo-nent in the mineral deposit as shows igures 3. and 4.)

Kriging Method

This method also comes under contributional meth-ods. It is often used to compute both local and global esti-mations of observed variables [8]. There is a basic equa-tion valuable for Kriging Method [7]:

1

*n

i iT w g= (2)

where:

T* - estimated value in the point B , g i - known value of observed variable ini-point,wi - weight of observation in thei-point.

Criterion of convenience of estimationT* against a realvalueT is the estimation of dispersion given by equation 3:

2

1

1 1

2 ,

, ,

n

i ii

n n

i j i ji j

w g A

w w g g A A

es g

g g

=

= =

= -

- -

(3)

where:

,ig Ag - average semivariogram between every point g i with known value of observer vari-able and estimated area A,

,i jg gg = - average semivariogram between the set of points with the known value of observedvariable,

, A Ag = - average semivariogram between each pointin an observed area A.

The concrete value of observed variable, computed byKriging Method depends on:- pattern geometry in an estimated area,- semivariogram model,- weights assigned to patterns.

There is a need of dispersion defect minimization for optimal estimation:

22 min 0

iw

ee

ss

- = (4)

.igure 3

Slika 3

Spatial position of mine and bores with values of benefi-cial component in patternsProstorni polo aj rudnika i bu otina s vrijednostima po-zitivne komponente u uzorcima

.igure 4

Slika 4

Spatial model of beneficial component distribution com-puted by Kriging MethodProstorni model prostiranja pozitivne komponente izra-unate metodom Kriginga


4/5

METALURGIJA43 (2004) 4, 287-291290


and after modification of equation 4:

2 10

i

i

w

w

e s l

- -

=

(5)

If we take all partial derivatives equal to zero, we get anequation system the results of which are weight ,i nw wK

with valuable basic condition1

1.n

ii

w=

=

Processing of large amounts of data by Kriging Methodis highly time-consuming also for computer. It takes froma few minutes to several hours to process input data (for thousands of input data), thus it is convenient to perform

those calculations on high performance PCs or special-ized either multiprocessor or multicomputer systems [3].

It is possible to shorten processing time demanded for large sets of data using both data-oriented parallelism andclusters.

ParallelGIS SYSTEM- STRUCTURE AND COMPONENTS

Practical application of geographical data processingusing interpolation methods and parallel architectures isParallelGIS system, being developed by Department of Computers and Informatics, aculty of Electrical Engineer-

ing and Informatics in cooperation with Department of GIS,

.igure 5Slika 5

ParallelGIS system architectureSustav arhitekture usporednog GIS-a

ParalelGIS Server

ParalelGIS Client ParalelGIS Client

ParalelGIS Client

. . .

Deposit name Number of analysed patterns Number of microblocks

Kobeliarovo1500 1000

Bankov - Ko ice 3200 3500Ki ovce - vbovce 150 900Jel ava 3800 4500Ro ava - Strieborn 117 250

Table 1Tablica 1

Mineral deposits sizeVeliina mineralnih depozita

BERG aculty on the Technical University of Ko ice,Slovakia [5, 6]. igure 5. shows the basic scheme of ParallelGIS system.

System is composed of two basic elements: ParallelGISServer a ParallelGIS Client.

ParallelGIS Server. Contains database of input dataordered in files. Data have been acquired by carring pat-terns analysis. Stored data are used for computing both beneficial ( e) and harmful component distribution insidethe deposit of iron ore. Information are provided byPGserver application installed on ParallelGIS server.

ParallelGIS Client. Provides analysis of data acquiredfrom ParallelGIS server. The analysis is performed byArcView system using Avenue programming language ableto process sets of geographical data. Data requesting and

receiving process from ParallelGIS server as well asArcView data processing initialization procedure and pro-cessed data send-back procedure are provided by PGclientapplication.

ParallelGIS SYSTEMAPPLICATION AND ACHIEVED RESULTS

Efficiency of data processing by ParallelGIS systemwas tested on a pattern of five randomly chosen mineraldeposits. There was one mineral deposit used in each test,it consists of distribution of both beneficial and harmfulcomponent modelling. Mineral deposit size was rated beamount of input data (a number of patterns from of min-eral deposit) and number of microblocks to which mineraldeposit was divided in the time of calculation as showsTable 1. (each mineral deposit was horizontally and verti-cally divided to several thousand microblocks, which rep-resent the basic mining unit (block) [9]).

There were five ParallelGIS Client computers and oneParallel GIS Server ( igure 5.) used in each test. The re-sult of each test was rated by the time demanded to inputdata processing - chemical analysis of patterns from min-eral deposit. The results are shown in igure 6. It also in-cludes time demanded for such amount of input data using

single processor system ( igure 6.).


5/5

Utilization of Parallel Computer System for Modeling of Geological Phenomena in GIS

Documents

Transcript of Utilization of Parallel Computer System for Modeling of Geological Phenomena in GIS