Parallelism for Nested Loops with Nonuniform and Flow Dependences


 Melvin Allison
 1 years ago
 Views:
Transcription
1 Parallelsm for Nested Loops wth Nonunform and Flow Dependences SamJn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseodong, Cheonan, Chungnam, , Korea. Abstract. Many methods are proposed n order to parallelze loops wth nonunform dependence, but most of such approaches perform poorly due to rregular and complex dependence constrants. Ths paper proposes an effcent method of tlng and transformng nested loops wth nonunform and flow dependences for maxmzng parallelsm. Our approach s based on the Convex Hull theory that has adequate nformaton to handle nonunform dependences, and also based on mnmum dependence dstance tlng, the unque set orented parttonng, and three regon parttonng methods. We wll frst show how to fnd the ncrementng mnmum dependence dstance. Next, we wll propose how to tle the teraton space effcently accordng to the ncrementng mnmum dependence dstance. Fnally, we wll show how to acheve more parallelsm by loop nterchangng and how to transform t nto parallel loops. Comparson wth some other methods shows more parallelsm than other exstng methods. Introducton Parallel processng s recognzed as an mportant vehcle for the soluton of many areas of computer applcatons. Most of the computng tme s spent n loops n such applcatons. The exstng parallelzng complers can parallelze most of the loops wth unform dependences, but they do not satsfactorly handle loops wth nonunform dependences. Most of the tme, the compler leaves such loops runnng sequentally. Unfortunately, loops wth nonunform dependences are not so uncommon n the real world. Several works have been done for loops wth nonunform dependences. All of the exstng technques do a good ob for some partcular types of loops, but show us a poor performance on some other types of loops. Some technques, based on Convex Hull theory [7] that has been proven to have enough nformaton to handle nonunform dependences, are the mnmum dependence dstance tlng method [5], [6], the unque set orented parttonng method [4], and the three regon parttonng [], [3]. Ths paper wll focus on parallelzng perfectly nested loops wth nonunform and flow dependences. The rest of ths paper s organzed as follows. Secton two descrbes our loop model, and revews some fundamental concepts n nonunform and flow dependence M. Bubak et al. (Eds.): ICCS 2004, LNCS 3038, pp , SprngerVerlag Berln Hedelberg 2004
2 Parallelsm for Nested Loops wth Nonunform and Flow Dependences 47 loop. Secton three presents an mproved tlng method for parallelzaton wth nested loops wth nonunform and flow dependences. In ths secton, we show how to fnd the ncrementng mnmum dependence dstances n the teraton space. Then, we dscuss how to tle the teraton space effcently accordng to the ncrementng mnmum dependence dstance and how to acheve more parallelsm by loop nterchangng. Secton four shows comparson wth related works. Fnally, we conclude n secton fve wth the drecton to enhance ths work. 2 Data Dependence Analyss n Nonunform and Flow Dependence Loop The loop model consdered n ths paper s doubly nested loops wth lnearly coupled subscrpts and both lower and upper bounds for loop varables should be known at comple tme. The loop model has the form n Fg.. do = l, u do = l 2, u 2 A(a + b + c, a 2 + b 2 + c 2 ) = = A(a 2 + b 2 + c 2, a 22 + b 22 + c 22 ) Fg.. A doubly nested loop model The dependence dstance functon d ) n flow dependence loops gves the dependence dstances d ) and d ) n dmensons and, respectvely. For unform dependence vector sets these dstances are constant. But, for the nonunform dependence sets these dstances are lnear functons of the loop ndces. We can wrte these dependence dstance functons n a general form as d(, ) = (d (, ), d (, )) d (, ) = p * + q * + r d (, ) = p 2 * + q 2 * + r 2 where p, q, and r are real values and and are nteger varables of the teraton space. The propertes and theorems for tlng of nested loops wth flow dependence can be descrbed as follows. Theorem. If there s only flow dependence n the loop, DCH contans flow dependence tals and DCH2 contans flow dependence heads. Theorem 2. If there s only flow dependence n the loop, then d (x, y) = 0 or d (x, y) = 0 does not pass through any DCH. If there exsts only flow dependence n the loop, then d (x ) = 0 or d (x ) = 0 does not pass through any IDCH(Integer Dependence Convex Hull) because the IDCH s a subspace of DCH(Dependence Convex Hull) [5].
3 48 S.J. Jeong Theorem 3. If there s only flow dependence n the loop, the mnmum and maxmum values of the dependence dstance functon d(x ) appear on the extreme ponts. Theorem 4. If there s only flow dependence n the loop, the mnmum dependence dstance value d mn s equal or greater than zero. From theorem 4, we know that when there s only flow dependence n the loop and d mn s zero, d mn s greater than zero. In ths case, snce d (x ) = 0 does not pass through the IDCH, the mnmum value of d (x ), d mn, occurs at one of the extreme ponts. Theorem 5 If there s only flow dependence n the loop, the dfference between the dstance of a dependence and that of the next dependence, d nc, s equal to or greater than zero. Thus, d nc s equal to to or greater than zero when there s only flow dependence n the loop. 3 Improved Tlng Method Cho and Lee [2] present a more general and powerful loop splttng method to enhance all parallelsm on a sngle loop. The method uses more nformaton from the loop such as ncrement factors, and the dfference between the dstance of dependence, and that of the next dependence. Cho and Lee [3] derve an effcent method for nested loops wth smple scrpts from enhancng [2]. The mnmum dependence dstance tlng method [6] presents an algorthm to convert the extreme ponts wth real coordnates to the extreme ponts wth nteger coordnates. The method obtans an IDCH from a DCH. It can compute d mn, the mnmum value of the dependence dstance functon d ) and d mn, the mnmum value of the dependence dstance functon d ) from the extreme ponts of the IDCH. The frst mnmum dependence dstances d mn and d mn are used to determne the unform tle sze n the teraton space. 3. Tlng Method by the Incrementng Mnmum Dependence Dstance From theorem 5, when p > 0 and q 0, we know that the dfference between the dstance of a dependence and that of the next dependence n loop wth flow dependence, d nc, s equal to or greater than zero. For each, d mn s ncremented as the value of s ncremented. So, the second d mn s equal to or greater than the frst one, and the thrd one s greater than the second one, and so on. The mproved tlng method for doubly nested loops wth nonunform and flow dependence s descrbed as Procedure Tlng_Method, whch s the algorthm of tlng loop by the ncrementng mnmum dependence dstance as shown n Fg. 2. Ths algorthm computes the ncrementng mnmum dependence dstance, tles the teraton space effcently accordng to the ncrementng mnmum dependence dstance, and transforms t nto parallel loops.
4 Parallelsm for Nested Loops wth Nonunform and Flow Dependences 49 Procedure Tlng_Method(,, l, l 2, u, u 2, d )), : and value for the source of the frst mnmum dependence n the loop computed by the extreme ponts of the IDCH l, l 2, u, u 2 : the lower and upper bounds of outer loop and nner loop, respectvely d ): the dependence dstance functon of the IDCH begn Step : when the frst source pont, (, ), s gven, the frst mnmum dependence dstance d mn and frst tle sze are computed. Step 2: Next d mn s computed. If (next snk pont s greater than bound), Goto Step 4. Step 3: Next tle sze s computed, and Goto Step 2. Step 4: the orgnal loop s transformed nto n parallel tles. end Tlng_Method. Fg. 2. Algorthm of tlng loop by the ncrementng mnmum dependence dstence Example do =, 50 do =, 50 A(3*+, 4*+2*+) =......= A(2*4, +4) An example gven n Example llustrates the case that there s nonunform and flow dependence. Fg. 3(a) shows CDCH(Complete Dependence Convex Hull) of Example. As the example, we can obtan the followng results usng the mproved tlng method proposed n ths secton. 50 DCH DCH (a) (b) Fg. 3. (a) CDCH, (b) Tlng by mnmum dependence dstance n Example. 50
5 50 S.J. Jeong From the algorthm to compute a twodmensonal IDCH n [5], we can obtan the extreme ponts such as (, ), (, 22), and (8, ) as shown n Fg. 3(a). The frst mnmum value of d ) occurs at one of the extreme ponts. The value for the source of the frst dependence n the second tle s 4. The value n the thrd tle s 0, and next values are 9, 3, and 49. Then, we can dvde the teraton space by four tles as shown n Fg. 3(b). 3.2 Loop Tlng Method Usng Loop Interchangng When there s only flow dependence n the loop, we can tle the teraton space nto tles wth wdth = d mn or wdth = d mn. In case d mn > d mn, we can tle the teraton space nto tles wth wdth = d mn. In Example, because d ) (= 5/2* + + 5/2) s greater than d ) (= /2* + 5/2), we can use an changed form of the example that the outer loop and the nner loop are nterchanged as shown n Fg. 4. do =, 50 do =, 50 A(3*+, 4*+2*+) =......= A(2*4, +4) Fg. 4. Another form of Example by loop nterchangng. If the upper lmts of loop and are 00 by 00 as an example gven n Fg. 4, the number of tles for the orgnal loop s sx as shown n Fg. 5(a), and for the nterchanged loop s fve as shown n Fg. 5(b). When d mn > d mn n ths loop, we can acheve greater parallelsm by loop nterchangng (a) (b) Fg. 5. (a) Tlng by the ncrementng mnmum dependence dstance, (b) Tlng by Loop Interchangng n Example.
6 Parallelsm for Nested Loops wth Nonunform and Flow Dependences 5 4 Performance Analyss Ths secton dscusses the performance analyss of our proposed methods through the comparsons wth related works theoretcally. Theoretcal speedup for performance analyss can be computed as follows. Ignorng the synchronzaton, schedulng and varable renamng overheads, and assumng an unlmted number of processors, each partton can be executed n one tme step. Hence, the total tme of executon s equal to the number of parallel regons, N p, plus the number of sequental teratons, N s. Generally, speedup s represented by the rato of total sequental executon tme to the executon tme on parallel computer system as follows: Speedup = (N * N )/(N p + N s ) where N, N are the sze of loop,, respectvely We wll compare our proposed methods wth the mnmum dependence dstance tlng method and the unque set orented parttonng method as follows: Let's consder the loop shown n Example. Fg. 3(a) shows orgnal parttonng of Example. Ths example s the case that there s only flow dependence and DCH overlaps DCH2. Applyng the unque set orented parttonng to ths loop llustrates case 2 of [4]. Ths method can dvde the teraton space nto four regons: three parallel regons, AREA, AREA2 and AREA4, and one seral regon, AREA3, as shown n Fg. 6. The speedup for ths method s (00*00)/(3+44) = AREA AREA4 22 AREA2 AREA3 8 Fg. 6. Regons of the loop parttoned by the unque sets orented parttonng n Example. Applyng the mnmum dependence dstance tlng method to ths loop llustrates case of ths technque [5], whch s the case that lne d (, ) = 0 does not pass through the IDCH. The mnmum value of d (, ), d mn, occurs at the extreme pont (, ) and d mn = 3. The space can be tled wth wdth = 3, thus 34 tles are obtaned. The speedup for ths method s (00*00)/34 =294. Let s apply our proposed method  the mproved tlng method as gven n secton 3. Ths loop s tled by sx areas as shown n Fg. 5(a). The teratons wthn each area can be fully executed n parallel. So, the speedup for ths method s (00*00)/6 = 666.
7 52 S.J. Jeong Applyng the loop nterchangng method n ths example, ths loop s tled by fve areas as shown n Fg. 6(b). So, the speedup for ths method s (00*00)/5 = If the upper bounds of loop, are 000 by 000, the speedup for the orgnal loop s (000*000)/ = 90909, and the speedup for the nterchanged loop s (000*000)/8 = Because d mn > d mn n ths example, we can acheve more parallelsm by loop nterchangng. 5 Conclusons In ths paper, we have studed the problem of transformng nested loops wth nonunform and flow dependences to maxmze parallelsm. When there s only flow dependence n the loop, we propose the mproved tlng method. The mnmum dependence dstance tlng method tles the teraton space by the frst mnmum dependence dstance unformly. Our proposed method, however, tles the teraton space by mnmum dependence dstance values that are ncremented as the value of s ncremented. Furthermore, when d mn > d mn n the gven loop, loop parallelsm can be mproved by loop nterchangng. In comparson wth some prevous parttonng methods, the mproved tlng method gves much better speedup than the mnmum dependence dstance tlng method and the unque set orented parttonng method n the case that there s only flow dependence and DCH overlaps DCH2. Our future research work s to develop a method for mprovng parallelzaton of hgher dmensonal nested loops. References. A. A. Zaafran and M. R. Ito, "Parallel regon executon of loops wth rregular dependences," n Proceedngs of the Internatonal Conference on Parallel Processng, vol. II, (994) C. K. Cho, J. C. Shm, and M. H. Lee, "A loop transformaton for maxmzng parallelsm from sngle loops wth nonunform dependences," n Proceedngs of Hgh Performance Computng Asa '97, (997) C. K. Cho and M. H. Lee, "A loop parallzaton method for nested loops wth nonunform dependences", n Proceedngs of the Internatonal Conference on Parallel and Dstrbuted Systems, (997) J. Ju and V. Chaudhary, "Unque sets orented parttonng of nested loops wth nonunform dependences," n Proceedngs of Internatonal Conference on Parallel Processng, vol. III, (996) S. Punyamurtula and V. Chaudhary, "Mnmum dependence dstance tlng of nested loops wth nonunform dependences," n Proceedngs of Symposum on Parallel and Dstrbuted Processng, (994) S. Punyamurtula, V. Chaudhary, J. Ju, and S. Roy, "Comple tme parttonng of nested loop teraton spaces wth nonunform dependences," Journal of Parallel Algorthms and Applcatons, (996) 7. T. Tzen and L. N, "Dependence unformzaton: A loop parallelzaton technque," IEEE Transactons on Parallel and Dstrbuted Systems, vol. 4, no. 5, (993)
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 informationLoop 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 informationVectorization in the Polyhedral Model
Vectorzaton n the Polyhedral Model LousNoël Pouchet pouchet@cse.ohostate.edu Dept. of Computer Scence and Engneerng, the Oho State Unversty October 200 888. Introducton: Overvew Vectorzaton: Detecton
More informationToday Using FourierMotzkin elimination for code generation Using FourierMotzkin elimination for determining schedule constraints
Fourer Motzkn Elmnaton Logstcs HW10 due Frday Aprl 27 th Today Usng FourerMotzkn elmnaton for code generaton Usng FourerMotzkn elmnaton for determnng schedule constrants Unversty FourerMotzkn Elmnaton
More informationAn Optimal Algorithm for Prufer Codes *
J. Software Engneerng & Applcatons, 2009, 2: 111115 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 informationPositive Semidefinite Programming Localization in Wireless Sensor Networks
Postve Semdefnte Programmng Localzaton n Wreless Sensor etworks Shengdong Xe 1,, Jn Wang, Aqun Hu 1, Yunl Gu, Jang Xu, 1 School of Informaton Scence and Engneerng, Southeast Unversty, 10096, anjng Computer
More informationA 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 informationLoop Transformations, Dependences, and Parallelization
Loop Transformatons, Dependences, and Parallelzaton Announcements Mdterm s Frday from 34:15 n ths room Today Semester long project Data dependence recap Parallelsm and storage tradeoff Scalar expanson
More informationA Fast ContentBased Multimedia Retrieval Technique Using Compressed Data
A Fast ContentBased 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 informationThe Shortest Path of Touring Lines given in the Plane
Send Orders for Reprnts to reprnts@benthamscence.ae 262 The Open Cybernetcs & Systemcs Journal, 2015, 9, 262267 The Shortest Path of Tourng Lnes gven n the Plane Open Access Ljuan Wang 1,2, Dandan He
More informationImprovement 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, 631, Nuku, Katsuskaku,
More informationLoad Balancing for HexCell 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 HexCell Interconnecton Network Saher Manaseer,
More informationEdge Detection in Noisy Images Using the Support Vector Machines
Edge Detecton n Nosy Images Usng the Support Vector Machnes Hlaro GómezMoreno, Saturnno MaldonadoBascón, Francsco LópezFerreras Sgnal Theory and Communcatons Department. Unversty of Alcalá Crta. MadrdBarcelona
More informationContent Based Image Retrieval Using 2D Discrete Wavelet with Texture Feature with Different Classifiers
IOSR Journal of Electroncs and Communcaton Engneerng (IOSRJECE) eissn: 78834,p ISSN: 788735.Volume 9, Issue, Ver. IV (Mar  Apr. 04), PP 007 Content Based Image Retreval Usng D Dscrete Wavelet wth
More informationA New Approach For the Ranking of Fuzzy Sets With Different Heights
New pproach For the ankng of Fuzzy Sets Wth Dfferent Heghts Pushpnder Sngh School of Mathematcs Computer pplcatons Thapar Unversty, Patala7 00 Inda pushpndersnl@gmalcom STCT ankng of fuzzy sets plays
More informationClassifier Selection Based on Data Complexity Measures *
Classfer Selecton Based on Data Complexty Measures * Edth HernándezReyes, J.A. CarrascoOchoa, and J.Fco. MartínezTrndad Natonal Insttute for Astrophyscs, Optcs and Electroncs, Lus Enrque Erro No.1 Sta.
More informationPolyhedral Compilation Foundations
Polyhedral Complaton Foundatons LousNoël Pouchet pouchet@cse.ohostate.edu Dept. of Computer Scence and Engneerng, the Oho State Unversty Feb 8, 200 888., Class # Introducton: Polyhedral Complaton Foundatons
More informationThe 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 informationDetermining the Optimal Bandwidth Based on Multicriterion Fusion
Proceedngs of 01 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 5 (01) (01) IACSIT Press, Sngapore Determnng the Optmal Bandwdth Based on Multcrteron Fuson HaL Lang 1+, XanMn
More information2x 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 informationA 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 informationProblem 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 informationA 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 informationCompiler 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 informationSolving twoperson zerosum game by Matlab
Appled Mechancs and Materals Onlne: 20110202 ISSN: 16627482, Vols. 5051, pp 262265 do:10.4028/www.scentfc.net/amm.5051.262 2011 Trans Tech Publcatons, Swtzerland Solvng twoperson zerosum game by
More informationConstructing 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 informationRepeater Insertion for TwoTerminal Nets in ThreeDimensional Integrated Circuits
Repeater Inserton for TwoTermnal Nets n ThreeDmensonal Integrated Crcuts Hu Xu, Vasls F. Pavlds, and Govann De Mchel LSI  EPFL, CH5, Swtzerland, {hu.xu,vasleos.pavlds,govann.demchel}@epfl.ch Abstract.
More informationParallel matrixvector multiplication
Appendx A Parallel matrxvector multplcaton The reduced transton matrx of the threedmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more
More informationProgramming 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 informationSmoothing Spline ANOVA for variable screening
Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and multobjectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory
More informationThe 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 informationModule Management Tool in Software Development Organizations
Journal of Computer Scence (5): 8, 7 ISSN 5966 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. AlRababah and Mohammad A. AlRababah Faculty of IT, AlAhlyyah Amman Unversty,
More informationTerm Weighting Classification System Using the Chisquare Statistic for the Classification Subtask at NTCIR6 Patent Retrieval Task
Proceedngs of NTCIR6 Workshop Meetng, May 1518, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Chsquare Statstc for the Classfcaton Subtask at NTCIR6 Patent Retreval Task Kotaro Hashmoto
More informationA 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 ChunHung
More informationSLAM Summer School 2006 Practical 2: SLAM using Monocular Vision
SLAM Summer School 2006 Practcal 2: SLAM usng Monocular Vson Javer Cvera, Unversty of Zaragoza Andrew J. Davson, Imperal College London J.M.M Montel, Unversty of Zaragoza. josemar@unzar.es, jcvera@unzar.es,
More informationGSLM 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 informationSkew Angle Estimation and Correction of Hand Written, Textual and Large areas of NonTextual Document Images: A Novel Approach
Angle Estmaton and Correcton of Hand Wrtten, Textual and Large areas of NonTextual Document Images: A Novel Approach D.R.Ramesh Babu Pyush M Kumat Mahesh D Dhannawat PES Insttute of Technology Research
More informationSupport Vector Machines
/9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.
More informationAn 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 informationLLVM passes and Intro to Loop Transformation Frameworks
LLVM passes and Intro to Loop Transformaton Frameworks Announcements Ths class s recorded and wll be n D2L panapto. No quz Monday after sprng break. Wll be dong mdsemester class feedback. Today LLVM passes
More informationAn Application of the DulmageMendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices
Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 25492554 An Applcaton of the DulmageMendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal
More information2.1. The Program Model
Hyperplane Parttonng : n pproach to Global ata Parttonng for strbuted Memory Machnes S. R. Prakash and Y.. Srkant epartment of S, Indan Insttute of Scence angalore, Inda, 6 bstract utomatc Global ata Parttonng
More informationR 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 informationCluster Analysis of Electrical Behavior
Journal of Computer and Communcatons, 205, 3, 8893 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 informationA Deflected Gridbased Algorithm for Clustering Analysis
A Deflected Grdbased Algorthm for Clusterng Analyss NANCY P. LIN, CHUNGI CHANG, HAOEN CHUEH, HUNGJEN CHEN, WEIHUA HAO Department of Computer Scence and Informaton Engneerng Tamkang Unversty 5 Yngchuan
More informationA FivePoint Subdivision Scheme with Two Parameters and a FourPoint ShapePreserving Scheme
Mathematcal and Computatonal Applcatons Artcle A FvePont Subdvson Scheme wth Two Parameters and a FourPont ShapePreservng Scheme Jeqng Tan,2, Bo Wang, * and Jun Sh School of Mathematcs, Hefe Unversty
More information6.854 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 MAXSAT wth weghts, such that the
More informationTPLAware Displacementdriven Detailed Placement Refinement with Coloring Constraints
TPLware Dsplacementdrven Detaled Placement Refnement wth Colorng Constrants Tao Ln Iowa State Unversty tln@astate.edu Chrs Chu Iowa State Unversty cnchu@astate.edu BSTRCT To mnmze the effect of process
More informationA Modified Median Filter for the Removal of Impulse Noise Based on the Support Vector Machines
A Modfed Medan Flter for the Removal of Impulse Nose Based on the Support Vector Machnes H. GOMEZMORENO, S. MALDONADOBASCON, F. LOPEZFERRERAS, M. UTRILLA MANSO AND P. GILJIMENEZ Departamento de Teoría
More informationRelatedMode Attacks on CTR Encryption Mode
Internatonal Journal of Network Securty, Vol.4, No.3, PP.282 287, May 2007 282 RelatedMode Attacks on CTR Encrypton Mode Dayn Wang, Dongda Ln, and Wenlng Wu (Correspondng author: Dayn Wang) Key Laboratory
More informationLearningBased TopN Selection Query Evaluation over Relational Databases
LearnngBased TopN Selecton Query Evaluaton over Relatonal Databases Lang Zhu *, Wey Meng ** * School of Mathematcs and Computer Scence, Hebe Unversty, Baodng, Hebe 071002, Chna, zhu@mal.hbu.edu.cn **
More informationSum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints
Australan Journal of Basc and Appled Scences, 2(4): 12041208, 2008 ISSN 19918178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan
More informationDesign of Structure Optimization with APDL
Desgn of Structure Optmzaton wth APDL Yanyun School of Cvl Engneerng and Archtecture, East Chna Jaotong Unversty Nanchang 330013 Chna Abstract In ths paper, the desgn process of structure optmzaton wth
More informationUsing Fuzzy Logic to Enhance the Large Size Remote Sensing Images
Internatonal Journal of Informaton and Electroncs Engneerng Vol. 5 No. 6 November 015 Usng Fuzzy Logc to Enhance the Large Sze Remote Sensng Images Trung Nguyen Tu Huy Ngo Hoang and Thoa Vu Van Abstract
More informationA 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 informationThe relation between diamond tiling and hexagonal tiling
The relaton between damond tlng and hexagonal tlng Tobas Grosser INRIA and École Normale Supéreure, Pars tobas.grosser@nra.fr Sven Verdoolaege INRIA, École Normale Supéreure and KU Leuven sven.verdoolaege@nra.fr
More informationQuerying by sketch geographical databases. Yu Han 1, a *
4th Internatonal Conference on Sensors, Measurement and Intellgent Materals (ICSMIM 2015) Queryng by sketch geographcal databases Yu Han 1, a * 1 Department of Basc Courses, Shenyang Insttute of Artllery,
More informationNonSplit Restrained Dominating Set of an Interval Graph Using an Algorithm
Internatonal Journal of Advancements n Research & Technology, Volume, Issue, July ISS  onsplt Restraned Domnatng Set of an Interval Graph Usng an Algorthm ABSTRACT Dr.A.Sudhakaraah *, E. Gnana Deepka,
More informationFRACTAL COMPRESSION TECHNIQUE FOR COLOR IMAGES USING VARIABLE BLOCK
ISSN: 0976910 (ONLINE) ICTACT JOURNAL ON IMAGE AND VIDEO PROCESSING, NOVEMBER 017, VOLUME: 08, ISSUE: 0 DOI: 10.1917/jvp.017.030 FRACTAL COMPRESSION TECHNIQUE FOR COLOR IMAGES USING VARIABLE BLOCK Nsar
More informationBIN XIA et al: AN IMPROVED KMEANS ALGORITHM BASED ON CLOUD PLATFORM FOR DATA MINING
An Improved Kmeans Algorthm based on Cloud Platform for Data Mnng Bn Xa *, Yan Lu 2. School of nformaton and management scence, Henan Agrcultural Unversty, Zhengzhou, Henan 450002, P.R. Chna 2. College
More informationPerformance Study of Parallel Programming on Cloud Computing Environments Using MapReduce
Performance Study of Parallel Programmng on Cloud Computng Envronments Usng MapReduce WenChung Shh, ShanShyong Tseng Department of Informaton Scence and Applcatons Asa Unversty Tachung, 41354, Tawan
More informationLecture 5: Multilayer Perceptrons
Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented
More informationSteps for Computing the Dissimilarity, Entropy, HerfindahlHirschman and. Accessibility (Gravity with Competition) Indices
Steps for Computng the Dssmlarty, Entropy, HerfndahlHrschman and Accessblty (Gravty wth Competton) Indces I. Dssmlarty Index Measurement: The followng formula can be used to measure the evenness between
More informationA fast algorithm for color image segmentation
Unersty of Wollongong Research Onlne Faculty of Informatcs  Papers (Arche) Faculty of Engneerng and Informaton Scences 006 A fast algorthm for color mage segmentaton L. Dong Unersty of Wollongong, lju@uow.edu.au
More informationAlgorithmic Transformation Techniques for Efficient Exploration of Alternative Application Instances
In: Proc. 0th Int. Symposum on Hardware/Software Codesgn (CODES 02), Estes Park, Colorado, USA, May 6 8, 2002 Algorthmc Transformaton Technques for Effcent Exploraton of Alternatve Applcaton Instances
More informationCS 534: Computer Vision Model Fitting
CS 534: Computer Vson Model Fttng Sprng 004 Ahmed Elgammal Dept of Computer Scence CS 534 Model Fttng  1 Outlnes Model fttng s mportant Leastsquares fttng Maxmum lkelhood estmaton MAP estmaton Robust
More informationParallel Incremental Graph Partitioning Using Linear Programming
Syracuse Unversty SURFACE College of Engneerng and Computer Scence  Former Departments, Centers, Insttutes and roects College of Engneerng and Computer Scence 994 arallel Incremental Graph arttonng Usng
More informationWireless Sensor Network Localization Research
Sensors & Transducers 014 by IFSA Publshng, S L http://wwwsensorsportalcom Wreless Sensor Network Localzaton Research Lang Xn School of Informaton Scence and Engneerng, Hunan Internatonal Economcs Unversty,
More informationPrivate Information Retrieval (PIR)
2 Levente Buttyán Problem formulaton Alce wants to obtan nformaton from a database, but she does not want the database to learn whch nformaton she wanted e.g., Alce s an nvestor queryng a stockmarket
More informationLearning the Kernel Parameters in Kernel Minimum Distance Classifier
Learnng the Kernel Parameters n Kernel Mnmum Dstance Classfer Daoqang Zhang 1,, Songcan Chen and ZhHua Zhou 1* 1 Natonal Laboratory for Novel Software Technology Nanjng Unversty, Nanjng 193, Chna Department
More informationParallel Numerics. 1 Preconditioning & Iterative Solvers (From 2016)
Technsche Unverstät München WSe 6/7 Insttut für Informatk Prof. Dr. Thomas Huckle Dpl.Math. Benjamn Uekermann Parallel Numercs Exercse : Prevous Exam Questons Precondtonng & Iteratve Solvers (From 6)
More informationAssignment # 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 informationReport on Online Graph Coloring
2003 Fall Semester Comp 670K Onlne Algorthm Report on LO Yuet Me (00086365) cndylo@ust.hk Abstract Onlne algorthm deals wth data that has no future nformaton. Lots of examples demonstrate that onlne algorthm
More informationCommunicationMinimal Partitioning and Data Alignment for Af"ne Nested Loops
CommuncatonMnmal Parttonng and Data Algnment for Af"ne Nested Loops HYUKJAE 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 informationEcient Computation of the Most Probable Motion from Fuzzy. Moshe BenEzra Shmuel Peleg Michael Werman. The Hebrew University of Jerusalem
Ecent Computaton of the Most Probable Moton from Fuzzy Correspondences Moshe BenEzra Shmuel Peleg Mchael Werman Insttute of Computer Scence The Hebrew Unversty of Jerusalem 91904 Jerusalem, Israel Emal:
More informationRoutability Driven Modification Method of Monotonic Via Assignment for 2layer Ball Grid Array Packages
Routablty Drven Modfcaton Method of Monotonc Va Assgnment for 2layer Ball Grd Array Pacages Yoch Tomoa Atsush Taahash Department of Communcatons and Integrated Systems, Toyo Insttute of Technology 2 12
More informationX Chart Using ANOM Approach
ISSN 16848403 Journal of Statstcs Volume 17, 010, pp. 33 Abstract X Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are
More informationLoadBalanced Anycast Routing
LoadBalanced Anycast Routng ChngYu Ln, JungHua Lo, and SyYen Kuo Department of Electrcal Engneerng atonal Tawan Unversty, Tape, Tawan sykuo@cc.ee.ntu.edu.tw Abstract For faulttolerance and loadbalance
More informationBRDPHHC: A Balance RDF Data Partitioning Algorithm based on Hybrid Hierarchical Clustering
015 IEEE 17th Internatonal Conference on Hgh Performance Computng and Communcatons (HPCC), 015 IEEE 7th Internatonal Symposum on Cyberspace Safety and Securty (CSS), and 015 IEEE 1th Internatonal Conf
More informationVirtual Machine Migration based on Trust Measurement of Computer Node
Appled Mechancs and Materals Onlne: 20140404 ISSN: 16627482, Vols. 536537, pp 678682 do:10.4028/www.scentfc.net/amm.536537.678 2014 Trans Tech Publcatons, Swtzerland Vrtual Machne Mgraton based on
More informationParallel and Distributed Association Rule Mining  Dr. Giuseppe Di Fatta. San Vigilio,
Parallel and Dstrbuted Assocaton Rule Mnng  Dr. Guseppe D Fatta fatta@nf.unkonstanz.de San Vglo, 18092004 1 Overvew Assocaton Rule Mnng (ARM) Apror algorthm Hgh Performance Parallel and Dstrbuted Computng
More informationMathematics 256 a course in differential equations for engineering students
Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the
More informationThe Research of Support Vector Machine in Agricultural Data Classification
The Research of Support Vector Machne n Agrcultural Data Classfcaton Le Sh, Qguo Duan, Xnmng Ma, Me Weng College of Informaton and Management Scence, HeNan Agrcultural Unversty, Zhengzhou 45000 Chna Zhengzhou
More informationAn Image Compression Algorithm based on Wavelet Transform and LZW
An Image Compresson Algorthm based on Wavelet Transform and LZW Png Luo a, Janyong Yu b School of Chongqng Unversty of Posts and Telecommuncatons, Chongqng, 400065, Chna Abstract a cylpng@63.com, b y27769864@sna.cn
More informationImproving High Level Synthesis Optimization Opportunity Through Polyhedral Transformations
Improvng Hgh Level Synthess Optmzaton Opportunty Through Polyhedral Transformatons We Zuo 2,5, Yun Lang 1, Peng L 1, Kyle Rupnow 3, Demng Chen 2,3 and Jason Cong 1,4 1 Center for EnergyEffcent Computng
More informationClustering on antimatroids and convex geometries
Clusterng on antmatrods and convex geometres YULIA KEMPNER 1, ILYA MUCNIK 2 1 Department of Computer cence olon Academc Insttute of Technology 52 Golomb tr., P.O. Box 305, olon 58102 IRAEL 2 Department
More informationNUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS
ARPN Journal of Engneerng and Appled Scences 006017 Asan Research Publshng Network (ARPN). All rghts reserved. NUMERICAL SOLVING OPTIMAL CONTROL PROBLEMS BY THE METHOD OF VARIATIONS Igor Grgoryev, Svetlana
More informationOverview. Basic Setup [9] Motivation and Tasks. Modularization 2008/2/20 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION
Overvew 2 IMPROVED COVERAGE CONTROL USING ONLY LOCAL INFORMATION Introducton Mult Smulator MASIM Theoretcal Work and Smulaton Results Concluson Jay Wagenpfel, Adran Trachte Motvaton and Tasks Basc Setup
More informationTask Scheduling for Directed Cyclic Graph. Using Matching Technique
Contemporary Engneerng Scences, Vol. 8, 2015, no. 17, 773788 HIKARI Ltd, www.mhkar.com http://dx.do.org/10.12988/ces.2015.56193 Task Schedulng for Drected Cyclc Graph Usng Matchng Technque W.N.M. Arffn
More informationAdaptive Free Space Management of Online Placement for Reconfigurable Systems
Adaptve Free Space Management of Onlne Placement for Reconfgurable Systems TrongYen Lee, CheCheng Hu, and ChaChun Tsa Abstract The FPGA can be reconfgured both dynamcally and partally. Such reconfgurable
More informationSolitary 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. AlQudah Math epartment, Rabgh Faculty of
More informationA high precision collaborative vision measurement of gear chamfering profile
Internatonal Conference on Advances n Mechancal Engneerng and Industral Informatcs (AMEII 05) A hgh precson collaboratve vson measurement of gear chamferng profle Conglng Zhou, a, Zengpu Xu, b, Chunmng
More informationExplicit 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 informationEfficient Distributed Linear Classification Algorithms via the Alternating Direction Method of Multipliers
Effcent Dstrbuted Lnear Classfcaton Algorthms va the Alternatng Drecton Method of Multplers Caoxe Zhang Honglak Lee Kang G. Shn Department of EECS Unversty of Mchgan Ann Arbor, MI 48109, USA caoxezh@umch.edu
More informationParallel Solutions of Indexed Recurrence Equations
Parallel Solutons of Indexed Recurrence Equatons Yos BenAsher Dep of Math and CS Hafa Unversty 905 Hafa, Israel yos@mathcshafaacl Gad Haber IBM Scence and Technology 905 Hafa, Israel haber@hafascvnetbmcom
More informationVisual Curvature. 1. Introduction. y C. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2007
IEEE onf. on omputer Vson and Pattern Recognton (VPR June 7 Vsual urvature HaRong Lu, Longn Jan Lateck, WenYu Lu, Xang Ba HuaZhong Unversty of Scence and Technology, P.R. hna Temple Unversty, US lhrbss@gmal.com,
More informationPreconditioning 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 informationHighBoost Mesh Filtering for 3D Shape Enhancement
HghBoost Mesh Flterng for 3D Shape Enhancement Hrokazu Yagou Λ Alexander Belyaev y Damng We z Λ y z ; ; Shape Modelng Laboratory, Unversty of Azu, AzuWakamatsu 9658580 Japan y Computer Graphcs Group,
More informationUnsupervised Learning
Pattern Recognton Lecture 8 Outlne Introducton Unsupervsed Learnng Parametrc VS NonParametrc Approach Mxture of Denstes MaxmumLkelhood Estmates Clusterng Prof. Danel Yeung School of Computer Scence and
More informationMachine Learning 9. week
Machne Learnng 9. week Mappng Concept Radal Bass Functons (RBF) RBF Networks 1 Mappng It s probably the best scenaro for the classfcaton of two dataset s to separate them lnearly. As you see n the below
More information