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

Size: px
Start display at page:

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

Transcription

1 Loop Transformatons for Parallelsm & Localty Prevously Data dependences and loops Loop transformatons Parallelzaton Loop nterchange Today Loop nterchange Loop transformatons and transformaton frameworks Loop permutaton Loop reversal Loop skewng Loop fuson Loop Permutaton Swap the order of two loops to ncrease parallelsm, to mprove spatal localty, or to enable other transformatons Also known as loop nterchange x = A(2,) a row of A Ths access strdes through x = A(2,) Ths code s nvarant wth respect to the nner loop, yeldng better localty CS553 Lecture Loop Transformaton CS553 Lecture Loop Transformaton Loop Interchange (cont) x = A(,) Ths array has strde n access x = A(,) (Assumng column-maor order for Fortran) Ths array now has strde 1 access Legalty of Loop Interchange Case analyss of the drecton vectors (=,=) The dependence s loop ndependent, so t s unaffected by nterchange (=,<) The dependence s carred by the loop. After nterchange the dependence wll be (<,=), so the dependence wll stll be carred by the loop, so the dependence relatons do not change. (<,=) The dependence s carred by the loop. After nterchange the dependence wll be (=,<), so the dependence wll stll be carred by the loop, so the dependence relatons do not change. CS553 Lecture Loop Transformaton CS553 Lecture Loop Transformatons 4 1

2 Legalty of Loop Interchange (cont) Case analyss of the drecton vectors (cont.) (<,<) The dependence dstance s postve n both dmensons. After nterchange t wll stll be postve n both dmensons, so the dependence relatons do not change. (<,>) The dependence s carred by the outer loop. After nterchange the dependence wll be (>,<), whch changes the dependences and results n an llegal drecton vector, so nterchange s llegal. (>,*) (=,>) Such drecton vectors are not possble for the orgnal loop. Loop Interchange Consder the (<,>) case C(,) = C(+1,-1) Before (1,1) C(1,1) = C(2,0) (1,2) C(1,2) = C(2,1)... (2,1) C(2,1) = C(3,0) δ a C(,) = C(+1,-1) After (1,1) C(1,1) = C(2,0) (2,1) C(2,1) = C(3,0)... (1,2) C(1,2) = C(2,1) δ f CS553 Lecture Loop Transformatons 5 CS553 Lecture Loop Transformatons 6 Frameworks for Loop Transformatons Unmodular Loop Transformatons [Baneree 90],[Wolf & Lam 91] can represent loop permutaton, loop reversal, and loop skewng unmodular lnear mappng (determnant of matrx s + or - 1) T =, T s a matrx, and are teraton vectors transformaton s legal f the transformed dependence vector reman lexcographcally postve lmtatons only perfectly nested loops all statements are transformed the same CS553 Lecture Loop Transformatons 7 Legalty of Loop Interchange, Reprse Reduced case analyss of the drecton vectors (=,=) The dependence s loop ndependent, so t s unaffected by nterchange (=,<) The dependence s carred by the loop. After nterchange the dependence wll be (<,=), so the dependence wll stll be carred by the loop, so the dependence relatons do not change. (<,>) The dependence s carred by the outer loop. After nterchange the dependence wll be (>,<), whch changes the dependences and results n an llegal drecton vector, so nterchange s llegal. CS553 Lecture Loop Transformatons 8 2

3 Loop Reversal Change the drecton of loop teraton (.e., From low-to-hgh ndces to hgh-to-low ndces or vce versa) Benefts Improved cache performance Enables other transformatons (comng soon) do = 6,1,-1 A() = B() + C() A() = B() + C() CS553 Lecture Loop Transformatons 9 Loop Reversal and Dstance Vectors Impact Reversal of loop negates the th entry of all dstance vectors assocated wth the loop What about drecton vectors? When s reversal legal? When the loop beng reversed does not carry a dependence (.e., When the transformed dstance vectors reman legal) do = 1,5 do = 1,6 A(,) = A(-1,-1)+1 Transformed (1,1) (1,-1) legal CS553 Lecture Loop Transformaton0 Loop Reversal Loop Skewng Legalty Loop reversal wll change the drecton of the dependence relaton Is the followng legal? (1) Orgnal code do = 1,5 A(,) = A(-1,+1)+1 Dstance vector: (1, -1) Can we permute the orgnal loop? do = 6,1,-1 Ant (1) ( 1) Skewng: CS553 Lecture Loop Transformaton1 CS553 Lecture Loop Transformaton2 3

4 Transformng the Dependences and Array Accesses Transformng the Loop Bounds Orgnal code Orgnal code do = 1,5 A(,) = A(-1,+1)+1 Dependence vector: do = 1,5 A(,) = A(-1,+1)+1 Bounds: New Array Accesses: CS553 Lecture Loop Transformaton3 Transformed code do = 1,6 do = 1+,5+ A(, - ) = A( -1, - +1)+1 CS553 Lecture Loop Transformaton4 Loop Fuson Combne multple loop nests nto one B() = A()/2 B() = A()/2 Pros Cons May mprove data localty May hurt data localty Reduces loop overhead May hurt cache performance Enables array contracton (opposte of scalar expanson) May enable better nstructon schedulng CS553 Lecture Loop Transformaton5 Legalty of Loop Fuson Basc Condtons Both loops must have same structure Same loop depth Same loop bounds Can we relax any of these restrctons? Same teraton drectons Dependences must be preserved e.g., dependences must not become ant dependences body1 body2 All cross-loop dependences flow from body1 to body2 body1 body2 Ensure that fuson does not ntroduce dependences from body2 to body1 CS553 Lecture Loop Transformaton6 4

5 Loop Fuson What are the dependences? δ f δ f What are the dependences? δ f δ a Fuson changes the dependence between and, so fuson s llegal Is there some transformaton that wll enable fuson of these loops? Loop Fuson (cont) Loop reversal s legal for the orgnal loops Does not change the drecton of any dep n the orgnal code Wll reverse the drecton n the fused loop: δ a wll become δ f δ f δ f,-1 δ f δ f After reversal and fuson all orgnal dependences are preserved CS553 Lecture Loop Transformaton7 CS553 Lecture Loop Transformaton8 Concepts Usng drecton and dstance vectors Transformaton legalty (from prevous) must respect data dependences scalar expanson as a technque to remove ant and output dependences Next Tme Lecture More loop transformatons Another transformaton framework Transformatons: What s the beneft? What do they enable? When are they legal? Unmodular transformaton framework represents loop permutaton, loop reversal, and loop skewng provdes mathematcal framework for... testng transformaton legalty, transformng array accesses and loop bounds, and combnng transformatons CS553 Lecture Loop Transformaton9 CS553 Lecture Loop Transformaton0 5

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

Loop Transformations, Dependences, and Parallelization

Loop Transformations, Dependences, and Parallelization Loop Transformatons, Dependences, and Parallelzaton Announcements Mdterm s Frday from 3-4:15 n ths room Today Semester long project Data dependence recap Parallelsm and storage tradeoff Scalar expanson

More information

LLVM passes and Intro to Loop Transformation Frameworks

LLVM 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 md-semester class feedback. Today LLVM passes

More information

Vectorization in the Polyhedral Model

Vectorization in the Polyhedral Model Vectorzaton n the Polyhedral Model Lous-Noël Pouchet pouchet@cse.oho-state.edu Dept. of Computer Scence and Engneerng, the Oho State Unversty October 200 888. Introducton: Overvew Vectorzaton: Detecton

More information

Today Using Fourier-Motzkin elimination for code generation Using Fourier-Motzkin elimination for determining schedule constraints

Today Using Fourier-Motzkin elimination for code generation Using Fourier-Motzkin elimination for determining schedule constraints Fourer Motzkn Elmnaton Logstcs HW10 due Frday Aprl 27 th Today Usng Fourer-Motzkn elmnaton for code generaton Usng Fourer-Motzkn elmnaton for determnng schedule constrants Unversty Fourer-Motzkn Elmnaton

More information

Polyhedral Compilation Foundations

Polyhedral Compilation Foundations Polyhedral Complaton Foundatons Lous-Noël Pouchet pouchet@cse.oho-state.edu Dept. of Computer Scence and Engneerng, the Oho State Unversty Feb 8, 200 888., Class # Introducton: Polyhedral Complaton Foundatons

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

Lecture 15: Memory Hierarchy Optimizations. I. Caches: A Quick Review II. Iteration Space & Loop Transformations III.

Lecture 15: Memory Hierarchy Optimizations. I. Caches: A Quick Review II. Iteration Space & Loop Transformations III. Lecture 15: Memory Herarchy Optmzatons I. Caches: A Quck Revew II. Iteraton Space & Loop Transformatons III. Types of Reuse ALSU 7.4.2-7.4.3, 11.2-11.5.1 15-745: Memory Herarchy Optmzatons Phllp B. Gbbons

More information

U.C. Berkeley CS294: Beyond Worst-Case Analysis Handout 5 Luca Trevisan September 7, 2017

U.C. Berkeley CS294: Beyond Worst-Case Analysis Handout 5 Luca Trevisan September 7, 2017 U.C. Bereley CS294: Beyond Worst-Case Analyss Handout 5 Luca Trevsan September 7, 207 Scrbed by Haars Khan Last modfed 0/3/207 Lecture 5 In whch we study the SDP relaxaton of Max Cut n random graphs. Quc

More information

Support Vector Machines

Support 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 information

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe

CSCI 104 Sorting Algorithms. Mark Redekopp David Kempe CSCI 104 Sortng Algorthms Mark Redekopp Davd Kempe Algorthm Effcency SORTING 2 Sortng If we have an unordered lst, sequental search becomes our only choce If we wll perform a lot of searches t may be benefcal

More information

Computer Animation and Visualisation. Lecture 4. Rigging / Skinning

Computer Animation and Visualisation. Lecture 4. Rigging / Skinning Computer Anmaton and Vsualsaton Lecture 4. Rggng / Sknnng Taku Komura Overvew Sknnng / Rggng Background knowledge Lnear Blendng How to decde weghts? Example-based Method Anatomcal models Sknnng Assume

More information

ADJUSTING A PROGRAM TRANSFORMATION FOR LEGALITY

ADJUSTING A PROGRAM TRANSFORMATION FOR LEGALITY Parallel Processng Letters c World Scentfc Publshng Company ADJUSTING A PROGRAM TRANSFORMATION FOR LEGALITY CÉDRIC BASTOUL Laboratore PRSM, Unversté de Versalles Sant Quentn 45 avenue des États-Uns, 785

More information

Parallel Numerics. 1 Preconditioning & Iterative Solvers (From 2016)

Parallel 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 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

Outline. Discriminative classifiers for image recognition. Where in the World? A nearest neighbor recognition example 4/14/2011. CS 376 Lecture 22 1

Outline. Discriminative classifiers for image recognition. Where in the World? A nearest neighbor recognition example 4/14/2011. CS 376 Lecture 22 1 4/14/011 Outlne Dscrmnatve classfers for mage recognton Wednesday, Aprl 13 Krsten Grauman UT-Austn Last tme: wndow-based generc obect detecton basc ppelne face detecton wth boostng as case study Today:

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

Improving High Level Synthesis Optimization Opportunity Through Polyhedral Transformations

Improving 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 Energy-Effcent Computng

More information

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory Background EECS. Operatng System Fundamentals No. Vrtual Memory Prof. Hu Jang Department of Electrcal Engneerng and Computer Scence, York Unversty Memory-management methods normally requres the entre process

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

Feature Reduction and Selection

Feature Reduction and Selection Feature Reducton and Selecton Dr. Shuang LIANG School of Software Engneerng TongJ Unversty Fall, 2012 Today s Topcs Introducton Problems of Dmensonalty Feature Reducton Statstc methods Prncpal Components

More information

PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES

PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES Ruxandra Olmd Faculty of Mathematcs and Computer Scence, Unversty of Bucharest Emal: ruxandra.olmd@fm.unbuc.ro Abstract Vsual secret sharng schemes

More information

Private Information Retrieval (PIR)

Private 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 stock-market

More information

LECTURE NOTES Duality Theory, Sensitivity Analysis, and Parametric Programming

LECTURE NOTES Duality Theory, Sensitivity Analysis, and Parametric Programming CEE 60 Davd Rosenberg p. LECTURE NOTES Dualty Theory, Senstvty Analyss, and Parametrc Programmng Learnng Objectves. Revew the prmal LP model formulaton 2. Formulate the Dual Problem of an LP problem (TUES)

More information

The relation between diamond tiling and hexagonal tiling

The 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 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

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming

Kent State University CS 4/ Design and Analysis of Algorithms. Dept. of Math & Computer Science LECT-16. Dynamic Programming CS 4/560 Desgn and Analyss of Algorthms Kent State Unversty Dept. of Math & Computer Scence LECT-6 Dynamc Programmng 2 Dynamc Programmng Dynamc Programmng, lke the dvde-and-conquer method, solves problems

More information

Loop Transformations! Part II!

Loop Transformations! Part II! Lecture 9! Loop Transformations! Part II! John Cavazos! Dept of Computer & Information Sciences! University of Delaware! www.cis.udel.edu/~cavazos/cisc879! Loop Unswitching Hoist invariant control-flow

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

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

APPLICATION OF A COMPUTATIONALLY EFFICIENT GEOSTATISTICAL APPROACH TO CHARACTERIZING VARIABLY SPACED WATER-TABLE DATA

APPLICATION OF A COMPUTATIONALLY EFFICIENT GEOSTATISTICAL APPROACH TO CHARACTERIZING VARIABLY SPACED WATER-TABLE DATA RFr"W/FZD JAN 2 4 1995 OST control # 1385 John J Q U ~ M Argonne Natonal Laboratory Argonne, L 60439 Tel: 708-252-5357, Fax: 708-252-3 611 APPLCATON OF A COMPUTATONALLY EFFCENT GEOSTATSTCAL APPROACH TO

More information

Review. Loop Fusion Example

Review. Loop Fusion Example Review Distance vectors Concisely represent dependences in loops (i.e., in iteration spaces) Dictate what transformations are legal e.g., Permutation and parallelization Legality A dependence vector is

More information

Lecture 5: Multilayer Perceptrons

Lecture 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 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

Machine Learning. Support Vector Machines. (contains material adapted from talks by Constantin F. Aliferis & Ioannis Tsamardinos, and Martin Law)

Machine Learning. Support Vector Machines. (contains material adapted from talks by Constantin F. Aliferis & Ioannis Tsamardinos, and Martin Law) Machne Learnng Support Vector Machnes (contans materal adapted from talks by Constantn F. Alfers & Ioanns Tsamardnos, and Martn Law) Bryan Pardo, Machne Learnng: EECS 349 Fall 2014 Support Vector Machnes

More information

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

Tiling: A Data Locality Optimizing Algorithm

Tiling: A Data Locality Optimizing Algorithm Tiling: A Data Locality Optimizing Algorithm Announcements Monday November 28th, Dr. Sanjay Rajopadhye is talking at BMAC Friday December 2nd, Dr. Sanjay Rajopadhye will be leading CS553 Last Monday Kelly

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

Face Recognition University at Buffalo CSE666 Lecture Slides Resources:

Face Recognition University at Buffalo CSE666 Lecture Slides Resources: Face Recognton Unversty at Buffalo CSE666 Lecture Sldes Resources: http://www.face-rec.org/algorthms/ Overvew of face recognton algorthms Correlaton - Pxel based correspondence between two face mages Structural

More information

The Polyhedral Model (Transformations)

The Polyhedral Model (Transformations) The Polyhedral Model (Transformations) Announcements HW4 is due Wednesday February 22 th Project proposal is due NEXT Friday, extension so that example with tool is possible (see resources website for

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

Improving Low Density Parity Check Codes Over the Erasure Channel. The Nelder Mead Downhill Simplex Method. Scott Stransky

Improving Low Density Parity Check Codes Over the Erasure Channel. The Nelder Mead Downhill Simplex Method. Scott Stransky Improvng Low Densty Party Check Codes Over the Erasure Channel The Nelder Mead Downhll Smplex Method Scott Stransky Programmng n conjuncton wth: Bors Cukalovc 18.413 Fnal Project Sprng 2004 Page 1 Abstract

More information

Agenda & Reading. Simple If. Decision-Making Statements. COMPSCI 280 S1C Applications Programming. Programming Fundamentals

Agenda & Reading. Simple If. Decision-Making Statements. COMPSCI 280 S1C Applications Programming. Programming Fundamentals Agenda & Readng COMPSCI 8 SC Applcatons Programmng Programmng Fundamentals Control Flow Agenda: Decsonmakng statements: Smple If, Ifelse, nested felse, Select Case s Whle, DoWhle/Untl, For, For Each, Nested

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

SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR

SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR SENSITIVITY ANALYSIS IN LINEAR PROGRAMMING USING A CALCULATOR Judth Aronow Rchard Jarvnen Independent Consultant Dept of Math/Stat 559 Frost Wnona State Unversty Beaumont, TX 7776 Wnona, MN 55987 aronowju@hal.lamar.edu

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

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

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

Harmonic Coordinates for Character Articulation PIXAR

Harmonic Coordinates for Character Articulation PIXAR Harmonc Coordnates for Character Artculaton PIXAR Pushkar Josh Mark Meyer Tony DeRose Bran Green Tom Sanock We have a complex source mesh nsde of a smpler cage mesh We want vertex deformatons appled to

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

Efficient Code Generation for Automatic Parallelization and Optimization

Efficient Code Generation for Automatic Parallelization and Optimization Effcent Code Generaton for utomatc Parallelzaton and Optmzaton Cédrc Bastoul Laboratore PRSM, Unversté de Versalles Sant Quentn 5 avenue des États-Uns, 7805 Versalles Cedex, France Emal: cedrcbastoul@prsmuvsqfr

More information

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain AMath 483/583 Lecture 21 May 13, 2011 Today: OpenMP and MPI versons of Jacob teraton Gauss-Sedel and SOR teratve methods Next week: More MPI Debuggng and totalvew GPU computng Read: Class notes and references

More information

Hybrid Non-Blind Color Image Watermarking

Hybrid Non-Blind Color Image Watermarking Hybrd Non-Blnd Color Image Watermarkng Ms C.N.Sujatha 1, Dr. P. Satyanarayana 2 1 Assocate Professor, Dept. of ECE, SNIST, Yamnampet, Ghatkesar Hyderabad-501301, Telangana 2 Professor, Dept. of ECE, AITS,

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

Face Recognition Method Based on Within-class Clustering SVM

Face Recognition Method Based on Within-class Clustering SVM Face Recognton Method Based on Wthn-class Clusterng SVM Yan Wu, Xao Yao and Yng Xa Department of Computer Scence and Engneerng Tong Unversty Shangha, Chna Abstract - A face recognton method based on Wthn-class

More information

Loop Transformations, Dependences, and Parallelization

Loop Transformations, Dependences, and Parallelization Loop Transformations, Dependences, and Parallelization Announcements HW3 is due Wednesday February 15th Today HW3 intro Unimodular framework rehash with edits Skewing Smith-Waterman (the fix is in!), composing

More information

Optimization and Parallelization of Sequential Programs

Optimization and Parallelization of Sequential Programs DF Advanced Compler Constructon TDDC86 Compler optmzatons and code generaton Optmzaton and Parallelzaton of Sequental Programs Lecture 7 Chrstoph Kessler IDA / PELAB Lnköpng Unversty Sweden Outlne Towards

More information

12/2/2009. Announcements. Parametric / Non-parametric. Case-Based Reasoning. Nearest-Neighbor on Images. Nearest-Neighbor Classification

12/2/2009. Announcements. Parametric / Non-parametric. Case-Based Reasoning. Nearest-Neighbor on Images. Nearest-Neighbor Classification Introducton to Artfcal Intellgence V22.0472-001 Fall 2009 Lecture 24: Nearest-Neghbors & Support Vector Machnes Rob Fergus Dept of Computer Scence, Courant Insttute, NYU Sldes from Danel Yeung, John DeNero

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

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

Reading. 14. Subdivision curves. Recommended:

Reading. 14. Subdivision curves. Recommended: eadng ecommended: Stollntz, Deose, and Salesn. Wavelets for Computer Graphcs: heory and Applcatons, 996, secton 6.-6., A.5. 4. Subdvson curves Note: there s an error n Stollntz, et al., secton A.5. Equaton

More information

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters

Proper Choice of Data Used for the Estimation of Datum Transformation Parameters Proper Choce of Data Used for the Estmaton of Datum Transformaton Parameters Hakan S. KUTOGLU, Turkey Key words: Coordnate systems; transformaton; estmaton, relablty. SUMMARY Advances n technologes and

More information

REDUCING hardware design time is more than ever a

REDUCING hardware design time is more than ever a TCAD-2012-0168 1 Polyhedral Bubble Inserton: A Method to Improve Nested Loop Ppelnng for Hgh-Level Synthess Antone Morvan, Steven Derren, and Patrce Qunton Abstract Hgh-Level Synthess (HLS) allows hardware

More information

PROJECTIVE RECONSTRUCTION OF BUILDING SHAPE FROM SILHOUETTE IMAGES ACQUIRED FROM UNCALIBRATED CAMERAS

PROJECTIVE RECONSTRUCTION OF BUILDING SHAPE FROM SILHOUETTE IMAGES ACQUIRED FROM UNCALIBRATED CAMERAS PROJECTIVE RECONSTRUCTION OF BUILDING SHAPE FROM SILHOUETTE IMAGES ACQUIRED FROM UNCALIBRATED CAMERAS Po-Lun La and Alper Ylmaz Photogrammetrc Computer Vson Lab Oho State Unversty, Columbus, Oho, USA -la.138@osu.edu,

More information

Cache Performance 3/28/17. Agenda. Cache Abstraction and Metrics. Direct-Mapped Cache: Placement and Access

Cache Performance 3/28/17. Agenda. Cache Abstraction and Metrics. Direct-Mapped Cache: Placement and Access Agenda Cache Performance Samra Khan March 28, 217 Revew from last lecture Cache access Assocatvty Replacement Cache Performance Cache Abstracton and Metrcs Address Tag Store (s the address n the cache?

More information

FEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur

FEATURE EXTRACTION. Dr. K.Vijayarekha. Associate Dean School of Electrical and Electronics Engineering SASTRA University, Thanjavur FEATURE EXTRACTION Dr. K.Vjayarekha Assocate Dean School of Electrcal and Electroncs Engneerng SASTRA Unversty, Thanjavur613 41 Jont Intatve of IITs and IISc Funded by MHRD Page 1 of 8 Table of Contents

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

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

Empirical Distributions of Parameter Estimates. in Binary Logistic Regression Using Bootstrap

Empirical Distributions of Parameter Estimates. in Binary Logistic Regression Using Bootstrap Int. Journal of Math. Analyss, Vol. 8, 4, no. 5, 7-7 HIKARI Ltd, www.m-hkar.com http://dx.do.org/.988/jma.4.494 Emprcal Dstrbutons of Parameter Estmates n Bnary Logstc Regresson Usng Bootstrap Anwar Ftranto*

More information

Optimizing Document Scoring for Query Retrieval

Optimizing Document Scoring for Query Retrieval Optmzng Document Scorng for Query Retreval Brent Ellwen baellwe@cs.stanford.edu Abstract The goal of ths project was to automate the process of tunng a document query engne. Specfcally, I used machne learnng

More information

Classification / Regression Support Vector Machines

Classification / Regression Support Vector Machines Classfcaton / Regresson Support Vector Machnes Jeff Howbert Introducton to Machne Learnng Wnter 04 Topcs SVM classfers for lnearly separable classes SVM classfers for non-lnearly separable classes SVM

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

On Some Entertaining Applications of the Concept of Set in Computer Science Course

On Some Entertaining Applications of the Concept of Set in Computer Science Course On Some Entertanng Applcatons of the Concept of Set n Computer Scence Course Krasmr Yordzhev *, Hrstna Kostadnova ** * Assocate Professor Krasmr Yordzhev, Ph.D., Faculty of Mathematcs and Natural Scences,

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

Sorting. Sorted Original. index. index

Sorting. Sorted Original. index. index 1 Unt 16 Sortng 2 Sortng Sortng requres us to move data around wthn an array Allows users to see and organze data more effcently Behnd the scenes t allows more effectve searchng of data There are MANY

More information

Monte Carlo Rendering

Monte Carlo Rendering Monte Carlo Renderng Last Tme? Modern Graphcs Hardware Cg Programmng Language Gouraud Shadng vs. Phong Normal Interpolaton Bump, Dsplacement, & Envronment Mappng Cg Examples G P R T F P D Today Does Ray

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

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

Variant Multi Objective Tsp Model

Variant Multi Objective Tsp Model Volume Issue 06 Pages-656-670 June-06 ISSN e): 395-70 Varant Mult Objectve Tsp Model K.Vjaya Kumar, P.Madhu Mohan Reddy, C. Suresh Babu, M.Sundara Murthy Department of Mathematcs, Sr Venkateswara Unversty,

More information

RESEARCH ON EQUIVALNCE OF SPATIAL RELATIONS IN AUTOMATIC PROGRESSIVE CARTOGRAPHIC GENERALIZATION

RESEARCH ON EQUIVALNCE OF SPATIAL RELATIONS IN AUTOMATIC PROGRESSIVE CARTOGRAPHIC GENERALIZATION RESEARCH ON EQUIVALNCE OF SPATIAL RELATIONS IN AUTOMATIC PROGRESSIVE CARTOGRAPHIC GENERALIZATION Guo Qngsheng Du Xaochu Wuhan Unversty Wuhan Unversty ABSTRCT: In automatc cartographc generalzaton, the

More information

5 The Primal-Dual Method

5 The Primal-Dual Method 5 The Prmal-Dual Method Orgnally desgned as a method for solvng lnear programs, where t reduces weghted optmzaton problems to smpler combnatoral ones, the prmal-dual method (PDM) has receved much attenton

More information

An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc.

An Indian Journal FULL PAPER ABSTRACT KEYWORDS. Trade Science Inc. [Type text] [Type text] [Type text] ISSN : 97-735 Volume Issue 9 BoTechnology An Indan Journal FULL PAPER BTAIJ, (9), [333-3] Matlab mult-dmensonal model-based - 3 Chnese football assocaton super league

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

Concurrent Apriori Data Mining Algorithms

Concurrent Apriori Data Mining Algorithms Concurrent Apror Data Mnng Algorthms Vassl Halatchev Department of Electrcal Engneerng and Computer Scence York Unversty, Toronto October 8, 2015 Outlne Why t s mportant Introducton to Assocaton Rule Mnng

More information

D DAVID PUBLISHING. Advances in Ordinary Kriging Using the Stampede and Bridges Supercomputers. 1. Introduction. 2. Definitions from Ordinary Kriging

D DAVID PUBLISHING. Advances in Ordinary Kriging Using the Stampede and Bridges Supercomputers. 1. Introduction. 2. Definitions from Ordinary Kriging Journal of Geologcal Resource and Engneerng 6 (018) 14-18 do:10.1765/38-193/018.01.00 D DAVID PUBLISHING Advances n Ordnary Krgng Usng the Stampede and Brdges Supercomputers Ern M. Hodgess and Kendra Mhoon

More information

Efficient Broadcast Disks Program Construction in Asymmetric Communication Environments

Efficient Broadcast Disks Program Construction in Asymmetric Communication Environments Effcent Broadcast Dsks Program Constructon n Asymmetrc Communcaton Envronments Eleftheros Takas, Stefanos Ougaroglou, Petros copoltds Department of Informatcs, Arstotle Unversty of Thessalonk Box 888,

More information

Module Management Tool in Software Development Organizations

Module Management Tool in Software Development Organizations Journal of Computer Scence (5): 8-, 7 ISSN 59-66 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. Al-Rababah and Mohammad A. Al-Rababah Faculty of IT, Al-Ahlyyah Amman Unversty,

More information

A new selection strategy for selective cluster ensemble based on Diversity and Independency

A new selection strategy for selective cluster ensemble based on Diversity and Independency A new selecton strategy for selectve cluster ensemble based on Dversty and Independency Muhammad Yousefnezhad a, Al Rehanan b, Daoqang Zhang a and Behrouz Mnae-Bdgol c a Department of Computer Scence,

More information

A Probabilistic Approach to Detect Urban Regions from Remotely Sensed Images Based on Combination of Local Features

A Probabilistic Approach to Detect Urban Regions from Remotely Sensed Images Based on Combination of Local Features A Probablstc Approach to Detect Urban Regons from Remotely Sensed Images Based on Combnaton of Local Features Berl Sırmaçek German Aerospace Center (DLR) Remote Sensng Technology Insttute Weßlng, 82234,

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

Smoothing Spline ANOVA for variable screening

Smoothing Spline ANOVA for variable screening Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory

More information

Analysis on the Workspace of Six-degrees-of-freedom Industrial Robot Based on AutoCAD

Analysis on the Workspace of Six-degrees-of-freedom Industrial Robot Based on AutoCAD Analyss on the Workspace of Sx-degrees-of-freedom Industral Robot Based on AutoCAD Jn-quan L 1, Ru Zhang 1,a, Fang Cu 1, Q Guan 1 and Yang Zhang 1 1 School of Automaton, Bejng Unversty of Posts and Telecommuncatons,

More information

PHOTOGRAMMETRIC ANALYSIS OF ASYNCHRONOUSLY ACQUIRED IMAGE SEQUENCES

PHOTOGRAMMETRIC ANALYSIS OF ASYNCHRONOUSLY ACQUIRED IMAGE SEQUENCES PHOTOGRAMMETRIC ANALYSIS OF ASYNCHRONOUSLY ACQUIRED IMAGE SEQUENCES Karsten Raguse 1, Chrstan Hepke 2 1 Volkswagen AG, Research & Development, Dept. EZTV, Letter Box 1788, 38436 Wolfsburg, Germany Emal:

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

Biostatistics 615/815

Biostatistics 615/815 The E-M Algorthm Bostatstcs 615/815 Lecture 17 Last Lecture: The Smplex Method General method for optmzaton Makes few assumptons about functon Crawls towards mnmum Some recommendatons Multple startng ponts

More information

Ecient Computation of the Most Probable Motion from Fuzzy. Moshe Ben-Ezra Shmuel Peleg Michael Werman. The Hebrew University of Jerusalem

Ecient Computation of the Most Probable Motion from Fuzzy. Moshe Ben-Ezra Shmuel Peleg Michael Werman. The Hebrew University of Jerusalem Ecent Computaton of the Most Probable Moton from Fuzzy Correspondences Moshe Ben-Ezra Shmuel Peleg Mchael Werman Insttute of Computer Scence The Hebrew Unversty of Jerusalem 91904 Jerusalem, Israel Emal:

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

Circuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL)

Circuit Analysis I (ENGR 2405) Chapter 3 Method of Analysis Nodal(KCL) and Mesh(KVL) Crcut Analyss I (ENG 405) Chapter Method of Analyss Nodal(KCL) and Mesh(KVL) Nodal Analyss If nstead of focusng on the oltages of the crcut elements, one looks at the oltages at the nodes of the crcut,

More information

Real-time Fault-tolerant Scheduling Algorithm for Distributed Computing Systems

Real-time Fault-tolerant Scheduling Algorithm for Distributed Computing Systems Real-tme Fault-tolerant Schedulng Algorthm for Dstrbuted Computng Systems Yun Lng, Y Ouyang College of Computer Scence and Informaton Engneerng Zheang Gongshang Unversty Postal code: 310018 P.R.CHINA {ylng,

More information

Multi-Resolution Geometric Fusion

Multi-Resolution Geometric Fusion Internatonal Conference on Recent Advances n 3-D Dgtal Imagng and Modellng, Ottawa, Canada May 12 15, 1997 Mult-Resoluton Geometrc Fuson Adran Hlton and John Illngworth Centre for Vson, Speech and Sgnal

More information