Chapter 1. Comparison of an O(N ) and an O(N log N ) N -body solver. Abstract

Size: px
Start display at page:

Download "Chapter 1. Comparison of an O(N ) and an O(N log N ) N -body solver. Abstract"

Transcription

1 Chapter 1 Comparson of an O(N ) and an O(N log N ) N -body solver Gavn J. Prngle Abstract In ths paper we compare the performance characterstcs of two 3-dmensonal herarchcal N-body solvers an O(N) and an O(N log N) solver. We present the executon tmes for numerous N-body force evaluatons usng the two methods, wth varous values of N and, where s the prescrbed error. We nd that the O(N log N) method s more suted to problems whch demand a hgh precson and large N. We then consder how parallelsaton aects the algorthms' relatve performance. 1 Introducton The N-body problem conssts of a collecton of N partcles each exertng a force upon one another. The Nth partcle s acted upon by the remanng (N? 1) partcles, hence the tme to compute the force actng on each partcle s O(N 2 ). There are a collecton of N-body solvers whch reduce the tme to compute the N-body problem by ntroducng approxmatons. In ths paper we compare the performance characterstcs of two 3- dmensonal herarchcal N-body solvers; an O(N) and an O(N log N) solver. Our O(N) method derves from the Greengard-Rokhln Fast Multpole Method (FMM) [5, 6]. Examples of other O(N) N-body solvers may be found n [17, 1, 10]. The O(N log N) method utlses the same framework as the FMM, but n a manner whch s analogous to the Barnes-Hut Algorthm (BHA) [3]. Both the FMM and the BHA are readly parallelsed and have been mplemented on a wde range of parallel computers [4, 7, 8, 9, 14, 15, 16, 17]. The basc noton behnd these algorthms s that a cluster of partcles s replaced by a sngle source, descrbed by a multpole expanson. The force eld exerted by the cluster can be approxmated by the force eld exerted by ths multpole source, provded that the dstance between the pont of evaluaton and the cluster s large enough. Moreover, as the dstance to the cluster ncreases, we may ncrease the radal sze of the multpole source. Ths dea s eected by delneatng the clusters wth a herarchy, or `tree', of cells. The derence n the order of the two algorthms les n the manner n whch the herarchy of cells s utlsed. In the O(N log N) method, the nteracton model may be descrbed as a `partcle-cell nteracton' model, that s, the force exerted on each partcle s approxmated by ts nteracton wth the multpole sources contaned n the cell herarchy. For the O(N) method, however, the nteracton model may be descrbed as a `cell-cell nteracton' model. Ths s not strctly true, as the sources contaned n the cell herarchy do not actually nteract, but ths nomenclature does elucdate the character of the method. In ths case, a local expanson s created for every cell n the herarchy, n terms of the multpole expansons. Mathematcs Department, Naper Unversty, Ednburgh, EH14 1DJ, SCOTLAND 1

2 2 Prngle, Gavn J. It would appear that ths O(N) method s qucker than the O(N log N) method; however, tme for executon also depends strongly on the precson requred by the calculaton and on other specc mplementaton detals. In ths paper we present the executon tmes for numerous N-body force evaluatons usng the two methods, wth varous values of N and, where s the prescrbed error. Care s taken to ensure that all other parameters are optmsed wth respect to N and. These nclude tree depth and the number of terms taken n each multpole expanson. From the resultng executon tmes, we are able to determne whch of the two methods, the O(N) or O(N log N) method, s faster for a gven N and. We then consder how parallelsaton aects the algorthms' relatve performance, as the larger memory resource allows for an extended N- space. 2 Informal descrpton of the two solvers. Ths secton attempts to gve a rough llustraton of both of the N-body solvers. A fully detaled descrpton of the O(N) solver, and the relevant mathematcal operators, may be found n [10]. Both the O(N) method and the O(N log N) method utlse the same herarchy of multpole expansons, whch s descrbed n the followng secton. 2.1 The herarchy of multpole expansons Partcles may be grouped together nto clusters and represented by a lst of coecents whch descrbes ther dstrbuton, namely a multpole expanson. Consder the case of nteractng gravtatonal partcles, as shown n gure 1. Suppose we have a cluster of m r 0?????? O Fg. 1. r A cluster of partcles. - R partcles of mass m at postons r 0 from the orgn. If jrj > jr 0 j 8, then the scalar potental,, at a pont of evaluaton, R, located at poston r s gven by X X 1X n P n (cos ); (1) (R) =?G m jr? r 0 j =?G jrj n=0 m r 0 r where P n (x) s the Legendre polynomal of degree n, s the angle subtended between the vectors r and r 0, and G s the gravtatonal constant. We assume that G=1 wthout loss of generalty. Eqn.(1) descrbes the potental as a multpole expanson centred about the orgn. If we ensure that all the partcles le wthn a sphere of radus a,.e. jr 0 j < a, and nsst that jrj = ca, for some c > 1, then r0 < 1. If p s the hghest order retaned n the r c multpole expanson, then t may be shown that the truncaton error s gven by (2) abs = 1 r X 1X n=p+1 m 1 c n P n (cos ) A jrj(c? 1) 1 c p ;

3 Comparson of two N -body solvers. 3 P where A = jm j. Hence the value of p requred to acheve a gven relatve precson, = jrj abs, may be calculated from A (3) p = d? log c ((c? 1))e; where c s calculated n terms of the dstance to a pont wthn the cluster and the cluster's radus,.e. c = jrj. Ths s descrbed n full n [13]. a As the dstance to a cluster of bodes ncreases, then the radus of ths multpole expanson may also ncrease. Ths dea s eected by delneatng the clusters wth a herarchy of cells. The cells are constructed by recursvely subdvdng the computatonal doman. In 3 dmensons, the entre doman s enclosed by a cube whch s then subdvded nto eght equal cubes. Ths subdvson s performed recursvely untl there s only a small number of partcles per cell. The top level s labelled level 0; hence at any partcular level l there are 8 l cubes; ths s known as a oct-tree. The total number of levels, n, employed by the mesh has a strong nuence over the executon tme and s dependent on N, p and the dstrbuton of partcles [10, 12]. At ths pont, t s necessary to ntroduce some termnology whch s relevant to both algorthms. At any level of renement l, a cell x s subdvded nto 8 cells, whch are located at level l + 1. The 8 cells are known as the chldren of x; x s known as the parent. Cells whch have no chldren are known as leaf cells. Cells whch le at the same level of renement as cell x are known as near-eld cells, provded that the centre of these cells les less than a radal dstance of 3d away from the centre of cell x, where d s the length of one sde of a cube. Ths gves a maxmum of 92 near-eld cells. The nteracton set of a cell x s dened as those cells outwth the near-eld cells, whch are the chldren of the neghbours of x's parent. For both our N-body solvers, a truncated multpole expanson s created for every cell n the herarchy that contans at least one partcle. The method of determnng the value of p requred to acheve a gven precson s smlar to that employed n the FMM. In ths case the multpole expansons are centred on the geometrc centre of each cell n the herarchy. The BHA, on the other hand, centres the multpole expanson at the cluster's `centre-ofmass'. Ths latter system s benecal for problems where the `strengths' of the partcles are all postve. When ths s the case, the dpole moment s dentcally zero, thus f only the rst term s taken from the multpole expanson, the error behaves as f both the rst and the second terms were taken. However, many N-body smulatons, such as Vortex Methods n CFD, requre both postve and negatve `strengths'. Moreover, the error whch arses from the use of the latter system s typcally much smaller than the prescrbed error n practce. Ths s due to the fact that the least upper bound to ths error must be calculated n terms of the worst possble dstrbuton of partcles, unlke the FMM [13]. Thus one may predetermne the run-tme error wth greater control f the multpole expanson s centred on the centre of the cell. The herarchy of cells s created by rst formng multpole moments for each leaf cell n terms of the partcles whch le theren. The Legendre polynomal n eqn.(1) s expanded exactly n terms of sphercal harmoncs va the Addton Theorem [6]. The multpole moments are gven n terms of these sphercal harmoncs. Multpole moments are not calculated for empty cells. The tree s then traversed to the top, level=0, creatng multpole moments for each cell n terms of the multpole moments assocated wth the 8 chld cells. Ths s performed systematcally and ecently, snce the multpole expansons are centred on the cells' centres.

4 4 Prngle, Gavn J. 2.2 Informal descrpton of the O(N log N ) solver. The O(N log N) solver proceeds as follows. Each cell n the tree s consdered n turn, startng at the coarsest level, level=0. If the cell s not n the near-eld of the cell whch contans the partcle, then the assocated multpole moments are used to approxmate the potental. The next level of renement s then consdered, where all cells whch are not part of the near-eld, and whch have not yet been accounted for, wll contrbute ther assocated expansons. For any cell x, the set of cells whch contrbute a potental to partcles n cell x, s the nteracton set of cell x. Thus the nteracton set need only be located once per cell, and not once per partcle. Once at the nest level, the only partcles whch have not yet contrbuted to the potental wll be the partcles whch resde n the near-eld leaf cells. The parwse nteractons between these partcles are summed drectly. Consder a cell n the herarchy, cell say, whch s to contrbute an approxmated potental to a partcular partcle. We calculate the dstance, r say, between the partcle and the centre of that cell, and the radus of the sphere whch crcumscrbes t, a say. By eqn.(3), and snce the multpole expanson s centred at the geometrc centre of the cell, we requre p terms to acheve a certan relatve precson,, such that p = d? log c ( (c? 1))e; where c = r a. Thus, the more dstant a cell n an nteracton set, the fewer terms wll be requred to acheve a specc precson. 2.3 Informal Descrpton of the O(N ) solver The O(N) solver s dentcal to the O(N log N) solver up to the pont where the multpole moments are calculated for every cell n the herarchy. At ths stage, n the case of the O(N) solver, local expansons are created for every cell n the tree (startng at the root cell, level=0). A cell's assocated local expansons descrbes the potental n that cell due to the partcles whch le outwth tself and ts near-eld cells. The local expanson of cell x s formed from the multpole moments assocated wth all the nteracton set of cell x, and from the local expanson moments assocated wth the parent cell of cell x. Local expansons are not computed for empty cells. Once at the nest level, each leaf cell has an assocated local expanson, whch s then evaluated at each of the partcles whch le theren. As wth the O(N log N) solver, the `drect' parwse summaton method s used to evaluate the potental due to partcles whch le n the near-eld leaf cells. When employng the multpole moments to form the local expansons, only p multpole moments are requred, where (4) p = d? log c ((c? 1))e; where c =?1, where a s the dstance between the centre of the cell assocated wth the local expanson, and the centre of the cell assocated wth the multpole expanson [10, 13]. Each local expanson has p terms, where p = max (p ). Note that, for both methods, the same value of p s requred to acheve the prescrbed precson. The symmetry nherent n the oct-tree s exploted to reduce the amount of computaton. When a local expanson s used to form the local expansons of a cells 8 chld cells, only one local expanson s formed. The remanng 7 local expansons are formed by multplyng the rst local expanson by a shftng vector. Ths technque reduces the operaton count for ths operaton from O(8p 4 ) to O(p 4 + 8p 2 ). Another element of symmetry s exploted. If cell j les n the nteracton set of cell say, then the opposte s also true; n set notaton,

5 Comparson of two N -body solvers. 5 cell 2 nt(cell j) ) cell j 2 nt(cell ) Thus, once the nteracton set cells have been located, ther assocated local expansons may also be calculated at the same tme. Ths s smlar to the parwse nteracton used n the `drect' method and reduces ths computaton by a factor of order 2. The nherent symmetry of sphercal harmoncs s also utlsed. If cell 2 nt(cell j) and cell les drectly above, or drectly below cell j, then the computaton s reduced substantally. Moreover, for both methods presented n ths paper, ths symmetry s also used to reduce () the amount of memory one requres, () the amount of calculaton to be performed and () the sze of messages to be passed n a parallel mplementaton, all by a factor of 2 [10]. 3 Results and Conclusons A large number of N-body force evaluatons are performed usng the two methods, wth N = f1 10, 2 10, 5 10 ; = 2; 3; 4; 5g for p = 0 (monopole term only), p = 1 (dpole moments), p = 2, 4, 7, 9 and 12, for = 10?1 ; 10?2 ; 10?3 ; 10?4 and 10?5 respectvely, where s the least upper bound to the error whch s dened a pror, cf. eqn.(4). Care s taken to ensure that all other parameters, such as tree depth, are optmsed wth respect to N and. The resultng executon tmes were produced on Sun IPC Workstatons, and from these `wall-clock' tmes we are able to determne whch of the two methods, the O(N) or O(N log N) method, s faster for a gven N and. The programs are tmed from the moment after the locatons and strengths of the partcles have been read from le, untl the tme at whch the nal potental has been evaluated. Two derent dstrbutons were used to compare the two methods; a unform dstrbuton of partcles over a unt cube, and a dstrbuton over the surface of a sphere, where the and of the partcles' sphercal coordnates are unformly dstrbuted over [0; ] and [0; 2] respectvely Tme (secs) Fg. 2. O(N log N) p = 2 O(N log N) p = 7 O(N 2 ) `drect' method O(N) p = 2 O(N) p = N The number of partcles Executon tmes usng a sphercal dstrbuton of partcles. Fgure 2 shows the executon tmes for the O(N log N) method for p = 2 and p = 7, the `drect' O(N 2 ) method and the O(N) method for p = 2 and p = 7, usng the sphercal dstrbuton. The executon tmes for the same set of parameters, but usng the unform dstrbuton, produces a very smlar graph. For both dstrbutons, the O(N) method was substantally slower than the O(N log N)

6 6 Prngle, Gavn J. method for N 2 [10; 500K] and p = 4, 7, 9 and 12. Usng the sphercal dstrbuton, wth p = 0; 1 and 2, the O(N) method became faster than the O(N log N) method when N = 20, 350 and 4500 respectvely. When the unform dstrbuton was employed, wth p = 0 and 1, the O(N) method became faster than the O(N log N) method when N = 50 and 400 respectvely. For p = 2, the two methods executed n approxmately the same tme for N 2 [10; 500K]. From these results we have concluded that for problems charactersed by ether type of dstrbuton, the O(N) method s faster for problems wth low precson and large N, such as some astrophyscal smulatons. Whereas the O(N log N) method s more suted to problems whch demand a large N and a hgh precson, such as the Vortex Methods n CFD, whch requre a hgh precson n order to mnmse numercally nduced nstabltes Parallelsaton. Consder a MIMD dstrbuted memory machne, usng a local doman decomposton, where the computatonal doman s dstrbuted evenly over the nodes [11]. The two methods dscussed n ths paper requre the same communcaton routnes, and send the same data between the same nodes,.e. the multpole moments, thus the parallel codes wll only der n a computaton whch s ndependent of nterprocessor communcatons. Therefore ths manner of parallelsaton wll not aect the relatve performance of the two methods. However, parallelsaton wll allow for an extended N? space due to the larger memory resource. Moreover, to to ensure a balanced load for problems where the dstrbuton s non-unform, a scattered doman decomposton should be used [2, 14, 16]. References [1] Anderson, C.R., SIAM J. Sc. Stat. Comput., 13, 4, p , July [2] Baden, S.B., Vortex Methods, Lecture Notes n Mathematcs, Sprnger Verlag, p96-119, [3] Barnes, J., Hut, P., Nature, 324, p , [4] Greengard, L. Gropp, W.L., Parallel Proc. for Sc. Comp., SIAM, p , [5] Greengard, L., Rokhln, V., J. Comp. Phys., 73, p , [6] Greengard, L., The Rapd Evaluaton of Potental Felds n Partcle Systems, MIT Press, [7] Leathrum, J.F., Board, J.A., The Parallel Fast Multpole Algorthm n Three Dmensons, Techncal Report, Duke Unversty, Aprl [8] Lustg, S.R., Crsty, J.J., Pensak, D.A., Materals Research Socety Symposum Proceedngs Seres, 278, Symp. on Comp. Methods n Mat. Sc., Aprl [9] Nyland, L.S., Prns, J.F., Ref, J.H., 2nd Symposum on Issues and Obstacles n the Practcal Implementaton of Parallel Algorthms and the Use of Parallel Machnes (DAGS'93), Hanover, N.H., June [10] Prngle, G.J., Ph.D. Thess, Naper Unversty, Ednburgh, [11], J. Future Generaton of Computng Systems, 104, Jan., [12], User Gude to the 3-dmensonal Fast Multpole Method, Techncal Report, Naper Unversty, Ednburgh, [13], Error Analyss of the Multpole Methods, Techncal Report, Naper Unversty, Ednburgh, [14] Salmon, J.K., Warren, M.S., Wnckelmans, G.S., Intl. J. Supercompter Appl., 8, 2, 1994, (to appear). [15] Schmdt, K.E., Lee, M.A., J. of Stat. Phys, 63, Nos. 5/6, [16] Sngh, J.P., Ph.D. Thess, Stanford Unversty, [17] Zhao, F., Johnson, S.L., J. Sc. Stat. Comput., 12, 6, Nov

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes

R s s f. m y s. SPH3UW Unit 7.3 Spherical Concave Mirrors Page 1 of 12. Notes SPH3UW Unt 7.3 Sphercal Concave Mrrors Page 1 of 1 Notes Physcs Tool box Concave Mrror If the reflectng surface takes place on the nner surface of the sphercal shape so that the centre of the mrror bulges

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

A Data-Parallel Implementation of O(N) Hierarchical N-body Methods

A Data-Parallel Implementation of O(N) Hierarchical N-body Methods A Data-Parallel Implementaton of O(N) Herarchcal N-body Methods The Harvard communty has made ths artcle openly avalable. Please share how ths access benefts you. Your story matters Ctaton Hu, Yu, and

More information

Dynamic wetting property investigation of AFM tips in micro/nanoscale

Dynamic wetting property investigation of AFM tips in micro/nanoscale Dynamc wettng property nvestgaton of AFM tps n mcro/nanoscale The wettng propertes of AFM probe tps are of concern n AFM tp related force measurement, fabrcaton, and manpulaton technques, such as dp-pen

More information

Load Balancing for Hex-Cell Interconnection Network

Load Balancing for Hex-Cell Interconnection Network Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,

More information

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers

Content Based Image Retrieval Using 2-D Discrete Wavelet with Texture Feature with Different Classifiers IOSR Journal of Electroncs and Communcaton Engneerng (IOSR-JECE) e-issn: 78-834,p- ISSN: 78-8735.Volume 9, Issue, Ver. IV (Mar - Apr. 04), PP 0-07 Content Based Image Retreval Usng -D Dscrete Wavelet wth

More information

Parallel matrix-vector multiplication

Parallel matrix-vector multiplication Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more

More information

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto

More information

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1) Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A

More information

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster

More information

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour 6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

3D vector computer graphics

3D vector computer graphics 3D vector computer graphcs Paolo Varagnolo: freelance engneer Padova Aprl 2016 Prvate Practce ----------------------------------- 1. Introducton Vector 3D model representaton n computer graphcs requres

More information

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide

Lobachevsky State University of Nizhni Novgorod. Polyhedron. Quick Start Guide Lobachevsky State Unversty of Nzhn Novgorod Polyhedron Quck Start Gude Nzhn Novgorod 2016 Contents Specfcaton of Polyhedron software... 3 Theoretcal background... 4 1. Interface of Polyhedron... 6 1.1.

More information

Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications 14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of

More information

Hierarchical clustering for gene expression data analysis

Hierarchical clustering for gene expression data analysis Herarchcal clusterng for gene expresson data analyss Gorgo Valentn e-mal: valentn@ds.unm.t Clusterng of Mcroarray Data. Clusterng of gene expresson profles (rows) => dscovery of co-regulated and functonally

More information

Analysis of Continuous Beams in General

Analysis of Continuous Beams in General Analyss of Contnuous Beams n General Contnuous beams consdered here are prsmatc, rgdly connected to each beam segment and supported at varous ponts along the beam. onts are selected at ponts of support,

More information

Cluster Analysis of Electrical Behavior

Cluster Analysis of Electrical Behavior Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School

More information

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques

More information

Design and Analysis of Algorithms

Design and Analysis of Algorithms Desgn and Analyss of Algorthms Heaps and Heapsort Reference: CLRS Chapter 6 Topcs: Heaps Heapsort Prorty queue Huo Hongwe Recap and overvew The story so far... Inserton sort runnng tme of Θ(n 2 ); sorts

More information

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty

More information

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices

Steps for Computing the Dissimilarity, Entropy, Herfindahl-Hirschman and. Accessibility (Gravity with Competition) Indices Steps for Computng the Dssmlarty, Entropy, Herfndahl-Hrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between

More information

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS

A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Proceedngs of the Wnter Smulaton Conference M E Kuhl, N M Steger, F B Armstrong, and J A Jones, eds A MOVING MESH APPROACH FOR SIMULATION BUDGET ALLOCATION ON CONTINUOUS DOMAINS Mark W Brantley Chun-Hung

More information

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,

More information

Query Clustering Using a Hybrid Query Similarity Measure

Query Clustering Using a Hybrid Query Similarity Measure Query clusterng usng a hybrd query smlarty measure Fu. L., Goh, D.H., & Foo, S. (2004). WSEAS Transacton on Computers, 3(3), 700-705. Query Clusterng Usng a Hybrd Query Smlarty Measure Ln Fu, Don Hoe-Lan

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements

2x x l. Module 3: Element Properties Lecture 4: Lagrange and Serendipity Elements Module 3: Element Propertes Lecture : Lagrange and Serendpty Elements 5 In last lecture note, the nterpolaton functons are derved on the bass of assumed polynomal from Pascal s trangle for the fled varable.

More information

Electrical analysis of light-weight, triangular weave reflector antennas

Electrical analysis of light-weight, triangular weave reflector antennas Electrcal analyss of lght-weght, trangular weave reflector antennas Knud Pontoppdan TICRA Laederstraede 34 DK-121 Copenhagen K Denmark Emal: kp@tcra.com INTRODUCTION The new lght-weght reflector antenna

More information

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique //00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy

More information

the nber of vertces n the graph. spannng tree T beng part of a par of maxmally dstant trees s called extremal. Extremal trees are useful n the mxed an

the nber of vertces n the graph. spannng tree T beng part of a par of maxmally dstant trees s called extremal. Extremal trees are useful n the mxed an On Central Spannng Trees of a Graph S. Bezrukov Unverstat-GH Paderborn FB Mathematk/Informatk Furstenallee 11 D{33102 Paderborn F. Kaderal, W. Poguntke FernUnverstat Hagen LG Kommunkatonssysteme Bergscher

More information

A parallel Poisson solver using the fast multipole method on networks of workstations

A parallel Poisson solver using the fast multipole method on networks of workstations A parallel Posson solver usng the fast multpole method on networks of workstatons June-Yub Lee (jylee@math.ewha.ac.kr, jylee@cms.nyu.edu) Dept. of Math, Ewha Womans Unversty, Seoul120-750, KOREA, Karpjoo

More information

CE 221 Data Structures and Algorithms

CE 221 Data Structures and Algorithms CE 1 ata Structures and Algorthms Chapter 4: Trees BST Text: Read Wess, 4.3 Izmr Unversty of Economcs 1 The Search Tree AT Bnary Search Trees An mportant applcaton of bnary trees s n searchng. Let us assume

More information

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,

More information

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS

NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS ARPN Journal of Engneerng and Appled Scences 006-017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana

More information

S1 Note. Basis functions.

S1 Note. Basis functions. S1 Note. Bass functons. Contents Types of bass functons...1 The Fourer bass...2 B-splne bass...3 Power and type I error rates wth dfferent numbers of bass functons...4 Table S1. Smulaton results of type

More information

The Codesign Challenge

The Codesign Challenge ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.

More information

High-Boost Mesh Filtering for 3-D Shape Enhancement

High-Boost Mesh Filtering for 3-D Shape Enhancement Hgh-Boost Mesh Flterng for 3-D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, Azu-Wakamatsu 965-8580 Japan y Computer Graphcs Group,

More information

O n processors in CRCW PRAM

O n processors in CRCW PRAM PARALLEL COMPLEXITY OF SINGLE SOURCE SHORTEST PATH ALGORITHMS Mshra, P. K. Department o Appled Mathematcs Brla Insttute o Technology, Mesra Ranch-8355 (Inda) & Dept. o Electroncs & Electrcal Communcaton

More information

Constructing Minimum Connected Dominating Set: Algorithmic approach

Constructing Minimum Connected Dominating Set: Algorithmic approach Constructng Mnmum Connected Domnatng Set: Algorthmc approach G.N. Puroht and Usha Sharma Centre for Mathematcal Scences, Banasthal Unversty, Rajasthan 304022 usha.sharma94@yahoo.com Abstract: Connected

More information

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 An Iteratve Soluton Approach to Process Plant Layout usng Mxed

More information

Communication-Minimal Partitioning and Data Alignment for Af"ne Nested Loops

Communication-Minimal Partitioning and Data Alignment for Afne Nested Loops Communcaton-Mnmal Parttonng and Data Algnment for Af"ne Nested Loops HYUK-JAE LEE 1 AND JOSÉ A. B. FORTES 2 1 Department of Computer Scence, Lousana Tech Unversty, Ruston, LA 71272, USA 2 School of Electrcal

More information

Related-Mode Attacks on CTR Encryption Mode

Related-Mode Attacks on CTR Encryption Mode Internatonal Journal of Network Securty, Vol.4, No.3, PP.282 287, May 2007 282 Related-Mode Attacks on CTR Encrypton Mode Dayn Wang, Dongda Ln, and Wenlng Wu (Correspondng author: Dayn Wang) Key Laboratory

More information

A One-Sided Jacobi Algorithm for the Symmetric Eigenvalue Problem

A One-Sided Jacobi Algorithm for the Symmetric Eigenvalue Problem P-Q- A One-Sded Jacob Algorthm for the Symmetrc Egenvalue Problem B. B. Zhou, R. P. Brent E-mal: bng,rpb@cslab.anu.edu.au Computer Scences Laboratory The Australan Natonal Unversty Canberra, ACT 000, Australa

More information

Solitary and Traveling Wave Solutions to a Model. of Long Range Diffusion Involving Flux with. Stability Analysis

Solitary and Traveling Wave Solutions to a Model. of Long Range Diffusion Involving Flux with. Stability Analysis Internatonal Mathematcal Forum, Vol. 6,, no. 7, 8 Soltary and Travelng Wave Solutons to a Model of Long Range ffuson Involvng Flux wth Stablty Analyss Manar A. Al-Qudah Math epartment, Rabgh Faculty of

More information

Non-Split Restrained Dominating Set of an Interval Graph Using an Algorithm

Non-Split Restrained Dominating Set of an Interval Graph Using an Algorithm Internatonal Journal of Advancements n Research & Technology, Volume, Issue, July- ISS - on-splt Restraned Domnatng Set of an Interval Graph Usng an Algorthm ABSTRACT Dr.A.Sudhakaraah *, E. Gnana Deepka,

More information

User Authentication Based On Behavioral Mouse Dynamics Biometrics

User Authentication Based On Behavioral Mouse Dynamics Biometrics User Authentcaton Based On Behavoral Mouse Dynamcs Bometrcs Chee-Hyung Yoon Danel Donghyun Km Department of Computer Scence Department of Computer Scence Stanford Unversty Stanford Unversty Stanford, CA

More information

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr)

Helsinki University Of Technology, Systems Analysis Laboratory Mat Independent research projects in applied mathematics (3 cr) Helsnk Unversty Of Technology, Systems Analyss Laboratory Mat-2.08 Independent research projects n appled mathematcs (3 cr) "! #$&% Antt Laukkanen 506 R ajlaukka@cc.hut.f 2 Introducton...3 2 Multattrbute

More information

Classifier Selection Based on Data Complexity Measures *

Classifier Selection Based on Data Complexity Measures * Classfer Selecton Based on Data Complexty Measures * Edth Hernández-Reyes, J.A. Carrasco-Ochoa, and J.Fco. Martínez-Trndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.

More information

Preconditioning Parallel Sparse Iterative Solvers for Circuit Simulation

Preconditioning Parallel Sparse Iterative Solvers for Circuit Simulation Precondtonng Parallel Sparse Iteratve Solvers for Crcut Smulaton A. Basermann, U. Jaekel, and K. Hachya 1 Introducton One mportant mathematcal problem n smulaton of large electrcal crcuts s the soluton

More information

F Geometric Mean Graphs

F Geometric Mean Graphs Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 2 (December 2015), pp. 937-952 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) F Geometrc Mean Graphs A.

More information

with `ook-ahead for Broadcast WDM Networks TR May 14, 1996 Abstract

with `ook-ahead for Broadcast WDM Networks TR May 14, 1996 Abstract HPeR-`: A Hgh Performance Reservaton Protocol wth `ook-ahead for Broadcast WDM Networks Vjay Svaraman George N. Rouskas TR-96-06 May 14, 1996 Abstract We consder the problem of coordnatng access to the

More information

Efficient Multidimensional Searching Routines for Music Information Retrieval

Efficient Multidimensional Searching Routines for Music Information Retrieval Effcent Multdmensonal Searchng Routnes for Musc Informaton Retreval Josh Ress Jean-Julen ucouturer Mark Sandler Department of Electrcal Engneerng Kng s College London Strand. London WC2 2LR UK Phone: +44

More information

A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems

A Unified Framework for Semantics and Feature Based Relevance Feedback in Image Retrieval Systems A Unfed Framework for Semantcs and Feature Based Relevance Feedback n Image Retreval Systems Ye Lu *, Chunhu Hu 2, Xngquan Zhu 3*, HongJang Zhang 2, Qang Yang * School of Computng Scence Smon Fraser Unversty

More information

(e.g., []). In such cases, both the grd generaton process and the soluton of the resultng lnear systems can be computatonally expensve. The lack of re

(e.g., []). In such cases, both the grd generaton process and the soluton of the resultng lnear systems can be computatonally expensve. The lack of re A Free-Space Adaptve FMM-ased PDE Solver n Three Dmensons H. Langston L. Greengard D. orn October, Abstract We present a kernel-ndependent, adaptve fast multpole method (FMM) of arbtrary order accuracy

More information

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following. Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal

More information

A Robust Method for Estimating the Fundamental Matrix

A Robust Method for Estimating the Fundamental Matrix Proc. VIIth Dgtal Image Computng: Technques and Applcatons, Sun C., Talbot H., Ourseln S. and Adraansen T. (Eds.), 0- Dec. 003, Sydney A Robust Method for Estmatng the Fundamental Matrx C.L. Feng and Y.S.

More information

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions

Sorting Review. Sorting. Comparison Sorting. CSE 680 Prof. Roger Crawfis. Assumptions Sortng Revew Introducton to Algorthms Qucksort CSE 680 Prof. Roger Crawfs Inserton Sort T(n) = Θ(n 2 ) In-place Merge Sort T(n) = Θ(n lg(n)) Not n-place Selecton Sort (from homework) T(n) = Θ(n 2 ) In-place

More information

Loop Permutation. Loop Transformations for Parallelism & Locality. Legality of Loop Interchange. Loop Interchange (cont)

Loop Permutation. Loop Transformations for Parallelism & Locality. Legality of Loop Interchange. Loop Interchange (cont) Loop Transformatons for Parallelsm & Localty Prevously Data dependences and loops Loop transformatons Parallelzaton Loop nterchange Today Loop nterchange Loop transformatons and transformaton frameworks

More information

Computer models of motion: Iterative calculations

Computer models of motion: Iterative calculations Computer models o moton: Iteratve calculatons OBJECTIVES In ths actvty you wll learn how to: Create 3D box objects Update the poston o an object teratvely (repeatedly) to anmate ts moton Update the momentum

More information

Modelling of curves and surfaces in polar. and Cartesian coordinates. G.Casciola and S.Morigi. Department of Mathematics, University of Bologna, Italy

Modelling of curves and surfaces in polar. and Cartesian coordinates. G.Casciola and S.Morigi. Department of Mathematics, University of Bologna, Italy Modellng of curves and surfaces n polar and Cartesan coordnates G.Cascola and S.Morg Department of Mathematcs, Unversty of Bologna, Italy Abstract A new class of splne curves n polar coordnates has been

More information

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching

A Fast Visual Tracking Algorithm Based on Circle Pixels Matching A Fast Vsual Trackng Algorthm Based on Crcle Pxels Matchng Zhqang Hou hou_zhq@sohu.com Chongzhao Han czhan@mal.xjtu.edu.cn Ln Zheng Abstract: A fast vsual trackng algorthm based on crcle pxels matchng

More information

X- Chart Using ANOM Approach

X- Chart Using ANOM Approach ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are

More information

Loop Transformations for Parallelism & Locality. Review. Scalar Expansion. Scalar Expansion: Motivation

Loop Transformations for Parallelism & Locality. Review. Scalar Expansion. Scalar Expansion: Motivation Loop Transformatons for Parallelsm & Localty Last week Data dependences and loops Loop transformatons Parallelzaton Loop nterchange Today Scalar expanson for removng false dependences Loop nterchange Loop

More information

c 2009 Society for Industrial and Applied Mathematics

c 2009 Society for Industrial and Applied Mathematics SIAM J. MATRIX ANAL. APPL. Vol. 31, No. 3, pp. 1382 1411 c 2009 Socety for Industral and Appled Mathematcs SUPERFAST MULTIFRONTAL METHOD FOR LARGE STRUCTURED LINEAR SYSTEMS OF EQUATIONS JIANLIN XIA, SHIVKUMAR

More information

AUTHOR QUERY FORM. Fax:

AUTHOR QUERY FORM. Fax: Our reference: YJCPH 3186 P-authorquery-v8 AUTHOR QUERY FORM Journal: YJCPH Please e-mal or fax your responses and any correctons to: E-mal: correctons.esnl@elsever.sps.co.n Artcle Number: 3186 Fax: +31

More information

Explicit Formulas and Efficient Algorithm for Moment Computation of Coupled RC Trees with Lumped and Distributed Elements

Explicit Formulas and Efficient Algorithm for Moment Computation of Coupled RC Trees with Lumped and Distributed Elements Explct Formulas and Effcent Algorthm for Moment Computaton of Coupled RC Trees wth Lumped and Dstrbuted Elements Qngan Yu and Ernest S.Kuh Electroncs Research Lab. Unv. of Calforna at Berkeley Berkeley

More information

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming Optzaton Methods: Integer Prograng Integer Lnear Prograng Module Lecture Notes Integer Lnear Prograng Introducton In all the prevous lectures n lnear prograng dscussed so far, the desgn varables consdered

More information

Computation of Ex-Core Detector Weighting Functions for VVER-440 Using MCNP5

Computation of Ex-Core Detector Weighting Functions for VVER-440 Using MCNP5 Computaton of Ex-Core Detector Weghtng Functons for VVER-440 Usng MCNP5 Gabrel Farkas, Jozef Lpka, Ján Haščík, Vladmír Slugeň Slovak Unversty of Technology, Faculty of Electrcal Engneerng and Informaton

More information

Analysis of 3D Cracks in an Arbitrary Geometry with Weld Residual Stress

Analysis of 3D Cracks in an Arbitrary Geometry with Weld Residual Stress Analyss of 3D Cracks n an Arbtrary Geometry wth Weld Resdual Stress Greg Thorwald, Ph.D. Ted L. Anderson, Ph.D. Structural Relablty Technology, Boulder, CO Abstract Materals contanng flaws lke nclusons

More information

GSLM Operations Research II Fall 13/14

GSLM Operations Research II Fall 13/14 GSLM 58 Operatons Research II Fall /4 6. Separable Programmng Consder a general NLP mn f(x) s.t. g j (x) b j j =. m. Defnton 6.. The NLP s a separable program f ts objectve functon and all constrants are

More information

CHAPTER 2 DECOMPOSITION OF GRAPHS

CHAPTER 2 DECOMPOSITION OF GRAPHS CHAPTER DECOMPOSITION OF GRAPHS. INTRODUCTION A graph H s called a Supersubdvson of a graph G f H s obtaned from G by replacng every edge uv of G by a bpartte graph,m (m may vary for each edge by dentfyng

More information

A SYSTOLIC APPROACH TO LOOP PARTITIONING AND MAPPING INTO FIXED SIZE DISTRIBUTED MEMORY ARCHITECTURES

A SYSTOLIC APPROACH TO LOOP PARTITIONING AND MAPPING INTO FIXED SIZE DISTRIBUTED MEMORY ARCHITECTURES A SYSOLIC APPROACH O LOOP PARIIONING AND MAPPING INO FIXED SIZE DISRIBUED MEMORY ARCHIECURES Ioanns Drosts, Nektaros Kozrs, George Papakonstantnou and Panayots sanakas Natonal echncal Unversty of Athens

More information

CHARUTAR VIDYA MANDAL S SEMCOM Vallabh Vidyanagar

CHARUTAR VIDYA MANDAL S SEMCOM Vallabh Vidyanagar CHARUTAR VIDYA MANDAL S SEMCOM Vallabh Vdyanagar Faculty Name: Am D. Trved Class: SYBCA Subject: US03CBCA03 (Advanced Data & Fle Structure) *UNIT 1 (ARRAYS AND TREES) **INTRODUCTION TO ARRAYS If we want

More information

Modeling, Manipulating, and Visualizing Continuous Volumetric Data: A Novel Spline-based Approach

Modeling, Manipulating, and Visualizing Continuous Volumetric Data: A Novel Spline-based Approach Modelng, Manpulatng, and Vsualzng Contnuous Volumetrc Data: A Novel Splne-based Approach Jng Hua Center for Vsual Computng, Department of Computer Scence SUNY at Stony Brook Talk Outlne Introducton and

More information

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points;

Subspace clustering. Clustering. Fundamental to all clustering techniques is the choice of distance measure between data points; Subspace clusterng Clusterng Fundamental to all clusterng technques s the choce of dstance measure between data ponts; D q ( ) ( ) 2 x x = x x, j k = 1 k jk Squared Eucldean dstance Assumpton: All features

More information

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach Data Representaton n Dgtal Desgn, a Sngle Converson Equaton and a Formal Languages Approach Hassan Farhat Unversty of Nebraska at Omaha Abstract- In the study of data representaton n dgtal desgn and computer

More information

Multiblock method for database generation in finite element programs

Multiblock method for database generation in finite element programs Proc. of the 9th WSEAS Int. Conf. on Mathematcal Methods and Computatonal Technques n Electrcal Engneerng, Arcachon, October 13-15, 2007 53 Multblock method for database generaton n fnte element programs

More information

PHYSICS-ENHANCED L-SYSTEMS

PHYSICS-ENHANCED L-SYSTEMS PHYSICS-ENHANCED L-SYSTEMS Hansrud Noser 1, Stephan Rudolph 2, Peter Stuck 1 1 Department of Informatcs Unversty of Zurch, Wnterthurerstr. 190 CH-8057 Zurch Swtzerland noser(stuck)@f.unzh.ch, http://www.f.unzh.ch/~noser(~stuck)

More information

CSE 326: Data Structures Quicksort Comparison Sorting Bound

CSE 326: Data Structures Quicksort Comparison Sorting Bound CSE 326: Data Structures Qucksort Comparson Sortng Bound Steve Setz Wnter 2009 Qucksort Qucksort uses a dvde and conquer strategy, but does not requre the O(N) extra space that MergeSort does. Here s the

More information

Outline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like:

Outline. Self-Organizing Maps (SOM) US Hebbian Learning, Cntd. The learning rule is Hebbian like: Self-Organzng Maps (SOM) Turgay İBRİKÇİ, PhD. Outlne Introducton Structures of SOM SOM Archtecture Neghborhoods SOM Algorthm Examples Summary 1 2 Unsupervsed Hebban Learnng US Hebban Learnng, Cntd 3 A

More information

Chapter 6 Programmng the fnte element method Inow turn to the man subject of ths book: The mplementaton of the fnte element algorthm n computer programs. In order to make my dscusson as straghtforward

More information

Edge Detection in Noisy Images Using the Support Vector Machines

Edge Detection in Noisy Images Using the Support Vector Machines Edge Detecton n Nosy Images Usng the Support Vector Machnes Hlaro Gómez-Moreno, Saturnno Maldonado-Bascón, Francsco López-Ferreras Sgnal Theory and Communcatons Department. Unversty of Alcalá Crta. Madrd-Barcelona

More information

Wavefront Reconstructor

Wavefront Reconstructor A Dstrbuted Smplex B-Splne Based Wavefront Reconstructor Coen de Vsser and Mchel Verhaegen 14-12-201212 2012 Delft Unversty of Technology Contents Introducton Wavefront reconstructon usng Smplex B-Splnes

More information

and NSF Engineering Research Center Abstract Generalized speedup is dened as parallel speed over sequential speed. In this paper

and NSF Engineering Research Center Abstract Generalized speedup is dened as parallel speed over sequential speed. In this paper Shared Vrtual Memory and Generalzed Speedup Xan-He Sun Janpng Zhu ICASE NSF Engneerng Research Center Mal Stop 132C Dept. of Math. and Stat. NASA Langley Research Center Msssspp State Unversty Hampton,

More information

Evaluation of an Enhanced Scheme for High-level Nested Network Mobility

Evaluation of an Enhanced Scheme for High-level Nested Network Mobility IJCSNS Internatonal Journal of Computer Scence and Network Securty, VOL.15 No.10, October 2015 1 Evaluaton of an Enhanced Scheme for Hgh-level Nested Network Moblty Mohammed Babker Al Mohammed, Asha Hassan.

More information

Local and Global Accessibility Evaluation with Tool Geometry

Local and Global Accessibility Evaluation with Tool Geometry 19 Local and Global Accessblty Evaluaton wth Tool Geometry Jnnan Wang 1, Chell A. Roberts 2 and Scott Danelson 3 1 Arzona State Unversty, wangn@asu.edu 2 Arzona State Unversty, chell.roberts@asu.edu 2

More information

Resolving Ambiguity in Depth Extraction for Motion Capture using Genetic Algorithm

Resolving Ambiguity in Depth Extraction for Motion Capture using Genetic Algorithm Resolvng Ambguty n Depth Extracton for Moton Capture usng Genetc Algorthm Yn Yee Wa, Ch Kn Chow, Tong Lee Computer Vson and Image Processng Laboratory Dept. of Electronc Engneerng The Chnese Unversty of

More information

Accounting for the Use of Different Length Scale Factors in x, y and z Directions

Accounting for the Use of Different Length Scale Factors in x, y and z Directions 1 Accountng for the Use of Dfferent Length Scale Factors n x, y and z Drectons Taha Soch (taha.soch@kcl.ac.uk) Imagng Scences & Bomedcal Engneerng, Kng s College London, The Rayne Insttute, St Thomas Hosptal,

More information

Construction of ROBDDs. area. that such graphs, under some conditions, can be easily manipulated.

Construction of ROBDDs. area. that such graphs, under some conditions, can be easily manipulated. A Study of Composton Schemes for Mxed Apply/Compose Based Constructon of s A Narayan 1 S P Khatr 1 J Jan 2 M Fujta 2 R K Brayton 1 A Sangovann-Vncentell 1 Abstract Reduced Ordered Bnary Decson Dagrams

More information

Isosurface Extraction in Time-varying Fields Using a Temporal Hierarchical Index Tree

Isosurface Extraction in Time-varying Fields Using a Temporal Hierarchical Index Tree Isosurface Extracton n Tme-varyng Felds Usng a Temporal Herarchcal Index Tree Han-We Shen MRJ Technology Solutons / NASA Ames Research Center Abstract Many hgh-performance sosurface extracton algorthms

More information

USING GRAPHING SKILLS

USING GRAPHING SKILLS Name: BOLOGY: Date: _ Class: USNG GRAPHNG SKLLS NTRODUCTON: Recorded data can be plotted on a graph. A graph s a pctoral representaton of nformaton recorded n a data table. t s used to show a relatonshp

More information

An Accurate Evaluation of Integrals in Convex and Non convex Polygonal Domain by Twelve Node Quadrilateral Finite Element Method

An Accurate Evaluation of Integrals in Convex and Non convex Polygonal Domain by Twelve Node Quadrilateral Finite Element Method Internatonal Journal of Computatonal and Appled Mathematcs. ISSN 89-4966 Volume, Number (07), pp. 33-4 Research Inda Publcatons http://www.rpublcaton.com An Accurate Evaluaton of Integrals n Convex and

More information

Classifier Swarms for Human Detection in Infrared Imagery

Classifier Swarms for Human Detection in Infrared Imagery Classfer Swarms for Human Detecton n Infrared Imagery Yur Owechko, Swarup Medasan, and Narayan Srnvasa HRL Laboratores, LLC 3011 Malbu Canyon Road, Malbu, CA 90265 {owechko, smedasan, nsrnvasa}@hrl.com

More information

Array transposition in CUDA shared memory

Array transposition in CUDA shared memory Array transposton n CUDA shared memory Mke Gles February 19, 2014 Abstract Ths short note s nspred by some code wrtten by Jeremy Appleyard for the transposton of data through shared memory. I had some

More information

Solving two-person zero-sum game by Matlab

Solving two-person zero-sum game by Matlab Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by

More information

CLASSIFICATION OF ULTRASONIC SIGNALS

CLASSIFICATION OF ULTRASONIC SIGNALS The 8 th Internatonal Conference of the Slovenan Socety for Non-Destructve Testng»Applcaton of Contemporary Non-Destructve Testng n Engneerng«September -3, 5, Portorož, Slovena, pp. 7-33 CLASSIFICATION

More information

Problem Set 3 Solutions

Problem Set 3 Solutions Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,

More information