FFT: An example of complex combinational circuits

Size: px
Start display at page:

Download "FFT: An example of complex combinational circuits"

Transcription

1 Lecture from 6.s195 taught i Fall 2013 Costructive Computer Architecture FFT: A example of complex combiatioal circuits Arvid Computer Sciece & Artificial Itelligece Lab. Massachusetts Istitute of Techology September 22, L0x-1

2 Cotributors to the course material Arvid, Rishiyur S. Nikhil, Joel Emer, Muralidara Vijayaraghava Staff ad studets i (Sprig 2013), 6.S195 (Fall 2012), 6.S078 (Sprig 2012) Asif Kha, Richard Ruhler, Sag Woo Ju, Abhiav Agarwal, Myro Kig, Kermi Flemig, Mig Liu, Li- Shiua Peh Exteral Prof Amey Karkare & studets at IIT Kapur Prof Jihog Kim & studets at Seoul Natio Uiversity Prof Derek Chiou, Uiversity of Texas at Austi Prof Yoav Etsio & studets at Techio September 22, L0x-2

3 Cotets FFT ad IFFT: Aother complex combiatioal circuit ad its folded implemetatios FFT: Coverts sigals from time domai to frequecy domai IFFT: Coverts sigals from frequecy domai to time domai Two calculatios are idetical- the same hardware ca be used New BSV cocepts structure type overloadig September 22, L0x-3

4 Combiatioal IFFT i0 i1 i2 i3 i4 i63 t 0 t 1 t 2 x16 * * * * Permute *j Permute Permute All umbers are complex ad represeted as two sixtee bit quatities. Fixed-poit arithmetic is used to reduce area, power,... out0 out1 out2 out3 out4 out63 t September 22, L0x-4

5 4-way Butterfly Node x 0 * + + x 1 x 2 x 3 t 0 t 1 t 2 t 3 * * * *i fuctio Vector#(4,Complex) bfly4 (Vector#(4,Complex) t, Vector#(4,Complex) x); t s (twiddle coefficiets) are mathematically derivable costats for each bfly4 ad deped upo the positio of bfly4 the i the etwork FFT ad IFFT calculatios differ oly i the use of Twiddle coefficiets i various butterfly odes September 22, L0x-5

6 BSV code: 4-way Butterfly fuctio Vector#(4,Complex#(s)) bfly4 (Vector#(4,Complex#(s)) t, Vector#(4,Complex#(s)) x); Vector#(4,Complex#(s)) m, y, z; m[0] = x[0] * t[0]; m[1] = x[1] * t[1]; m[2] = x[2] * t[2]; m[3] = x[3] * t[3]; y[0] = m[0] + m[2]; y[1] = m[0] m[2]; y[2] = m[1] + m[3]; y[3] = i*(m[1] m[3]); z[0] = y[0] + y[2]; z[1] = y[1] + y[3]; z[2] = y[0] y[2]; z[3] = y[1] y[3]; retur(z); edfuctio Note: Vector does ot mea storage; just a group of wires with ames * * * * *i + Polymorphic code: works o ay type of umbers for which *, + ad - have bee defied m y z September 22, L0x-6

7 Laguage otes: Sequetial assigmets Sometimes it is coveiet to reassig a variable (x is zero every where except i bits 4 ad 8): Bit#(32) x = 0; x[4] = 1; x[8] = 1; This will usually result i itroductio of muxes i a circuit as the followig example illustrates: Bit#(32) x = 0; let y = x+1; if(p) x = 100; let z = x+1; p x y z September 22, L0x-7

8 Complex Arithmetic Additio z R = x R + y R z I = x I + y I Multiplicatio z R = x R * y R - x I * y I z I = x R * y I + x I * y R September 22, L0x-8

9 Represetig complex umbers as a struct typedef struct{ It#(t) r; It#(t) i; } Complex#(umeric type t) derivig (Eq,Bits); Notice the Complex type is parameterized by the size of It chose to represet its real ad imagiary parts If x is a struct the its fields ca be selected by writig x.r ad x.i September 22, L0x-9

10 BSV code for Additio typedef struct{ It#(t) r; It#(t) i; } Complex#(umeric type t) derivig (Eq,Bits); fuctio Complex#(t) cadd (Complex#(t) x, Complex#(t) y); It#(t) real = x.r + y.r; It#(t) imag = x.i + y.i; retur(complex{r:real, i:imag}); edfuctio What is the type of this +? September 22, L0x-10

11 Overloadig (Type classes) The same symbol ca be used to represet differet but related operators usig Type classes A type class groups a buch of types with similarly amed operatios. For example, the type class Arith requires that each type belogig to this type class has operators +,-, *, / etc. defied We ca declare Complex type to be a istace of Arith type class September 22, L0x-11

12 Overloadig +, * istace Arith#(Complex#(t)); fuctio Complex#(t) \+ (Complex#(t) x, Complex#(t) y); It#(t) real = x.r + y.r; It#(t) imag = x.i + y.i; retur(complex{r:real, i:imag}); edfuctio fuctio Complex#(t) \* (Complex#(t) x, Complex#(t) y); It#(t) real = x.r*y.r x.i*y.i; It#(t) imag = x.r*y.i + x.i*y.r; retur(complex{r:real, i:imag}); edfuctio edistace The cotext allows the compiler to pick the appropriate defiitio of a operator September 22, L0x-12

13 Combiatioal IFFT i0 out0 i1 out1 i2 i3 i4 i63 x16 Permute Permute stage_f fuctio Permute out2 out3 out4 out63 fuctio Vector#(64, Complex#()) stage_f (Bit#(2) stage, Vector#(64, Complex#()) stage_i); fuctio Vector#(64, Complex#()) ifft (Vector#(64, Complex#()) i_data); repeat stage_f three times September 22, L0x-13

14 BSV Code: Combiatioal IFFT fuctio Vector#(64, Complex#()) ifft (Vector#(64, Complex#()) i_data); //Declare vectors Vector#(4,Vector#(64, Complex#())) stage_data; stage_data[0] = i_data; for (Bit#(2) stage = 0; stage < 3; stage = stage + 1) stage_data[stage+1] = stage_f(stage,stage_data[stage]); retur(stage_data[3]); edfuctio The for-loop is ufolded ad stage_f is ilied durig static elaboratio Note: o otio of loops or procedures durig executio September 22, L0x-14

15 BSV Code: Combiatioal IFFT- Ufolded fuctio Vector#(64, Complex#()) ifft (Vector#(64, Complex#()) i_data); //Declare vectors Vector#(4,Vector#(64, Complex#())) stage_data; stage_data[0] = i_data; for stage_data[1] (Bit#(2) stage = stage_f(0,stage_data[0]); = 0; < 3; = stage + 1) stage_data[2] stage_data[stage+1] = stage_f(1,stage_data[1]); = stage_f(stage,stage_data[stage]); stage_data[3] = stage_f(2,stage_data[2]); retur(stage_data[3]); edfuctio Stage_f ca be ilied ow; it could have bee ilied before loop ufoldig also. Does the order matter? September 22, L0x-15

16 BSV Code for stage_f fuctio Vector#(64, Complex#()) stage_f (Bit#(2) stage, Vector#(64, Complex#()) stage_i); Vector#(64, Complex#()) stage_temp, stage_out; for (Iteger i = 0; i < 16; i = i + 1) begi Iteger idx = i * 4; Vector#(4, Complex#()) x; x[0] = stage_i[idx]; x[1] = stage_i[idx+1]; x[2] = stage_i[idx+2]; x[3] = stage_i[idx+3]; let twid = gettwiddle(stage, fromiteger(i)); let y = bfly4(twid, x); stage_temp[idx] = y[0]; stage_temp[idx+1] = y[1]; stage_temp[idx+2] = y[2]; stage_temp[idx+3] = y[3]; ed //Permutatio for (Iteger i = 0; i < 64; i = i + 1) stage_out[i] = stage_temp[permute[i]]; retur(stage_out); edfuctio twid s are mathematically derivable costats September 22, L0x-16

17 Higher-order fuctios: Stage fuctios f1, f2 ad f3 fuctio f0(x)= stage_f(0,x); fuctio f1(x)= stage_f(1,x); fuctio f2(x)= stage_f(2,x); What is the type of f0(x)? fuctio Vector#(64, Complex) f0 (Vector#(64, Complex) x); September 22, L0x-17

18 Suppose we wat to reduce the area of the circuit i0 out0 i1 out1 i2 i3 i4 x16 Permute Permute Permute out2 out3 out4 i63 out63 Reuse the same circuit three times to reduce area September 22, L0x-18

19 Reusig a combiatioal block f f g f g Itroduce state elemets to hold itermediate values we expect: Throughput to Area to decrease less parallelism decrease reusig a block The clock eeds to ru faster for the same throughput September 22, L0x-19

20 Folded IFFT: Reusig the stage combiatioal circuit i0 i1 i2 Permute out0 out1 out2 i3 out3 i4 out4 i63 Stage Couter out63 September 22, L0x-20

21 Iput ad Output FIFOs If IFFT is implemeted as a sequetial circuit it may take several cycles to process a iput Sometimes it is coveiet to thik of iput ad output of a combiatioal fuctio beig coected to FIFOs iq IFFT outq FIFO operatios: eq whe the FIFO is ot full deq, first whe the FIFO is ot empty These operatios ca be performed oly whe the guard coditio is satisfied September 22, L0x-21

22 Folded implemetatio rules Disjoit firig coditios x iq f stage sreg outq rule foldedetry if (stage==0); sreg <= f(stage, iq.first()); stage <= stage+1; iq.deq(); otice stage is a dyamic edrule parameter ow! rule foldedcirculate if (stage!=0)&(stage<(-1)); sreg <= f(stage, sreg); stage <= stage+1; edrule rule foldedexit if (stage==-1); outq.eq(f(stage, sreg)); stage <= 0; edrule Each rule has some additioal implicit guard coditios associated with FIFO operatios o forloop September 22, L0x-22

23 Folded implemetatio expressed as a sigle rule x iq f stage sreg outq rule folded-pipelie (True); let sxi =?; if (stage==0) begi sxi= iq.first(); iq.deq(); ed else sxi= sreg; let sxout = f(stage,sxi); if (stage==-1) outq.eq(sxout); else sreg <= sxout; stage <= (stage==-1)? 0 : stage+1; edrule September 22, L0x-23

24 Shared Circuit gettwiddle0 gettwiddle1 gettwiddle2 stage twid The rest of stage_f, i.e. Bfly-4s ad permutatios (shared) sx The Twiddle costats ca be expressed i a table or i a case or ested case expressio September 22, L0x-24

25 Pipeliig Combiatioal IFFT 3 differet datasets i the pipelie i0 IFFT i+1 IFFT i IFFT i-1 out0 i1 out1 i2 i3 i4 x16 Permute Permute Permute out2 out3 out4 i63 Lot of area ad log combiatioal delay Folded or multi-cycle versio ca save area ad reduce the combiatioal delay but throughput per clock cycle gets worse Pipeliig: a method to icrease the circuit throughput by evaluatig multiple IFFTs out63 Next lecture September 22, L0x-25

26 Desig compariso C f1 f2 f3 Combiatioal iq outq F f Folded iq outq P f1 f2 f3 Pipelie iq outq Clock? Area? Throughput? Clock: C < P F Area: F < C < P Throughput: F < C < P September 22, L0x-26

27 Area estimates Tool: Syopsys Desig Compiler Comb. FFT Combiatioal area: Nocombiatioal area: 9279 Folded FFT Combiatioal area: Nocombiatioal area: Pipelied FFT Combiatioal area: Nocombiatioal area: Are the results surprisig? Why is folded implemetatio ot smaller? Explaatio: Because of costat propagatio optimizatio, each bfly4 gets reduced by 60% whe twiddle factors are specified. Folded desig disallows this optimizatio because of the sharig of bfly4 s September 22, L0x-27

28 Sytax: Vector of Registers Register suppose x ad y are both of type Reg. The x <= y meas x._write(y._read()) Vector of It x[i] meas sel(x,i) x[i] = y[j] meas x = update(x, i, sel(y,j)) Vector of Registers x[i] <= y[j] does ot work. The parser thiks it meas (sel(x,i)._read)._write(sel(y,j)._read), which will ot type check (x[i]) <= y[j] parses as sel(x,i)._write(sel(y,j)._read), ad works correctly Do t ask me why September 22, L0x-28

29 Optioal: Superfolded FFT September 22, L0x-29

30 Superfolded IFFT: Just oe Bfly-4 ode! Optioal i0 out0 i1 i2 i3 i4 Stage 0 to 2 Permute 64, 2-way Muxes out1 out2 out3 out4 i63 4, 16-way Muxes Idex: 0 to 15 4, 16-way DeMuxes out63 Idex == 15? f will be ivoked for 48 dyamic values of stage; each ivocatio will modify 4 umbers i sreg after 16 ivocatios a permutatio would be doe o the whole sreg September 22, L0x-30

31 Superfolded IFFT: stage fuctio f Bit#(2+4) (stage,i) fuctio Vector#(64, Complex) stage_f (Bit#(2) stage, Vector#(64, Complex) stage_i); Vector#(64, Complex#()) stage_temp, stage_out; for (Iteger i = 0; i < 16; i = i + 1) begi Bit#(2) stage Iteger idx = i * 4; let twid = gettwiddle(stage, fromiteger(i)); let y = bfly4(twid, stage_i[idx:idx+3]); stage_temp[idx] = y[0]; stage_temp[idx+1] = y[1]; stage_temp[idx+2] = y[2]; stage_temp[idx+3] = y[3]; ed //Permutatio for (Iteger i = 0; i < 64; i = i + 1) stage_out[i] = stage_temp[permute[i]]; retur(stage_out); edfuctio should be doe oly whe i=15 September 22, L0x-31

32 Code for the Superfolded stage fuctio Fuctio Vector#(64, Complex) f (Bit#(6) stagei, Vector#(64, Complex) stage_i); let i = stagei `mod` 16; let twid = gettwiddle(stagei `div` 16, i); let y = bfly4(twid, stage_i[i:i+3]); let stage_temp = stage_i; stage_temp[i] = y[0]; stage_temp[i+1] = y[1]; stage_temp[i+2] = y[2]; stage_temp[i+3] = y[3]; Oe Bfly-4 case let stage_out = stage_temp; if (i == 15) for (Iteger i = 0; i < 64; i = i + 1) stage_out[i] = stage_temp[permute[i]]; retur(stage_out); edfuctio September 22, L0x-32

Combinational Circuits in

Combinational Circuits in Combinational Circuits in Bluespec Arvind Computer Science & Artificial Intelligence Lab Massachusetts Institute of Technology L031 Bluespec: TwoLevel Compilation Bluespec (Objects, Types, Higherorder

More information

Combinational Circuits in

Combinational Circuits in Combinational Circuits in Bluespec Arvind Computer Science & Artificial Intelligence Lab Massachusetts Institute of Technology L031 Bluespec: TwoLevel Compilation Bluespec (Objects, Types, Higherorder

More information

Combinational Circuits in Bluespec

Combinational Circuits in Bluespec Combinational Circuits in Bluespec Arvind Computer Science & Artificial Intelligence Lab Massachusetts Institute of Technology http://csg.csail.mit.edu/6.375 L031 Combinational circuits are acyclic interconnections

More information

Combinational Circuits in Bluespec

Combinational Circuits in Bluespec Combinational Circuits in Bluespec Arvind Computer Science & Artificial Intelligence Lab Massachusetts Institute of Technology February 13, 2013 http://csg.csail.mit.edu/6.375 L031 Combinational circuits

More information

Simple Inelastic and

Simple Inelastic and Simple Inelastic and Folded Pipelines Arvind Computer Science & Artificial Intelligence Lab Massachusetts Institute of Technology For taking noted during the lecture L3-1 Pipelining a block C inq f1 f2

More information

Simple Inelastic and

Simple Inelastic and Simple Inelastic and Folded Pipelines Arvind Computer Science & Artificial Intelligence Lab Massachusetts Institute of Technology L04-1 Pipelining a block C f1 f2 f3 Combinational P f1 f2 f3 Pipeline FP

More information

Combinational Circuits and Simple Synchronous Pipelines

Combinational Circuits and Simple Synchronous Pipelines Combinational Circuits and Simple Synchronous Pipelines Arvind Computer Science & Artificial Intelligence Lab Massachusetts Institute of Technology L061 Bluespec: TwoLevel Compilation Bluespec (Objects,

More information

Chapter 3 Classification of FFT Processor Algorithms

Chapter 3 Classification of FFT Processor Algorithms Chapter Classificatio of FFT Processor Algorithms The computatioal complexity of the Discrete Fourier trasform (DFT) is very high. It requires () 2 complex multiplicatios ad () complex additios [5]. As

More information

Bluespec-3: Modules & Interfaces. Bluespec: State and Rules organized into modules

Bluespec-3: Modules & Interfaces. Bluespec: State and Rules organized into modules Bluespec-3: Modules & Iterfaces Arvid Computer Sciece & Artificial Itelligece Lab Massachusetts Istitute of Techology Based o material prepared by Bluespec Ic, Jauary 2005 February 28, 2005 L09-1 Bluespec:

More information

Pipelining combinational circuits

Pipelining combinational circuits Constructive Computer Architecture: Pipelining combinational circuits Arvind Computer Science & Artificial Intelligence Lab. Massachusetts Institute of Technology September 18, 2013 http://csg.csail.mit.edu/6.s195

More information

Behavioral Modeling in Verilog

Behavioral Modeling in Verilog Behavioral Modelig i Verilog COE 202 Digital Logic Desig Dr. Muhamed Mudawar Kig Fahd Uiversity of Petroleum ad Mierals Presetatio Outlie Itroductio to Dataflow ad Behavioral Modelig Verilog Operators

More information

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design

CSC 220: Computer Organization Unit 11 Basic Computer Organization and Design College of Computer ad Iformatio Scieces Departmet of Computer Sciece CSC 220: Computer Orgaizatio Uit 11 Basic Computer Orgaizatio ad Desig 1 For the rest of the semester, we ll focus o computer architecture:

More information

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0 Polyomial Fuctios ad Models 1 Learig Objectives 1. Idetify polyomial fuctios ad their degree 2. Graph polyomial fuctios usig trasformatios 3. Idetify the real zeros of a polyomial fuctio ad their multiplicity

More information

Fast Fourier Transform (FFT) Algorithms

Fast Fourier Transform (FFT) Algorithms Fast Fourier Trasform FFT Algorithms Relatio to the z-trasform elsewhere, ozero, z x z X x [ ] 2 ~ elsewhere,, ~ e j x X x x π j e z z X X π 2 ~ The DFS X represets evely spaced samples of the z- trasform

More information

6.175: Constructive Computer Architecture. Oct 7,

6.175: Constructive Computer Architecture. Oct 7, 6.175: Costructive Computer Architecture Tutorial 2 Advaced BSV Qua Nguye (Now uses the correct dates o PPT slides) T02-1 Admiistrivia Today is add date! Please test vlsifarm machies Remaiig labs schedule

More information

Chapter 4. Procedural Abstraction and Functions That Return a Value. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 4. Procedural Abstraction and Functions That Return a Value. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 4 Procedural Abstractio ad Fuctios That Retur a Value Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 4.1 Top-Dow Desig 4.2 Predefied Fuctios 4.3 Programmer-Defied Fuctios 4.4

More information

EE 459/500 HDL Based Digital Design with Programmable Logic. Lecture 13 Control and Sequencing: Hardwired and Microprogrammed Control

EE 459/500 HDL Based Digital Design with Programmable Logic. Lecture 13 Control and Sequencing: Hardwired and Microprogrammed Control EE 459/500 HDL Based Digital Desig with Programmable Logic Lecture 13 Cotrol ad Sequecig: Hardwired ad Microprogrammed Cotrol Refereces: Chapter s 4,5 from textbook Chapter 7 of M.M. Mao ad C.R. Kime,

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 18 Strategies for Query Processig Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio DBMS techiques to process a query Scaer idetifies

More information

Ones Assignment Method for Solving Traveling Salesman Problem

Ones Assignment Method for Solving Traveling Salesman Problem Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:

More information

top() Applications of Stacks

top() Applications of Stacks CS22 Algorithms ad Data Structures MW :00 am - 2: pm, MSEC 0 Istructor: Xiao Qi Lecture 6: Stacks ad Queues Aoucemets Quiz results Homework 2 is available Due o September 29 th, 2004 www.cs.mt.edu~xqicoursescs22

More information

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence _9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to

More information

AN OPTIMIZATION NETWORK FOR MATRIX INVERSION

AN OPTIMIZATION NETWORK FOR MATRIX INVERSION 397 AN OPTIMIZATION NETWORK FOR MATRIX INVERSION Ju-Seog Jag, S~ Youg Lee, ad Sag-Yug Shi Korea Advaced Istitute of Sciece ad Techology, P.O. Box 150, Cheogryag, Seoul, Korea ABSTRACT Iverse matrix calculatio

More information

Lecture 1: Introduction and Strassen s Algorithm

Lecture 1: Introduction and Strassen s Algorithm 5-750: Graduate Algorithms Jauary 7, 08 Lecture : Itroductio ad Strasse s Algorithm Lecturer: Gary Miller Scribe: Robert Parker Itroductio Machie models I this class, we will primarily use the Radom Access

More information

Major CSL Write your name and entry no on every sheet of the answer script. Time 2 Hrs Max Marks 70

Major CSL Write your name and entry no on every sheet of the answer script. Time 2 Hrs Max Marks 70 NOTE:. Attempt all seve questios. Major CSL 02 2. Write your ame ad etry o o every sheet of the aswer script. Time 2 Hrs Max Marks 70 Q No Q Q 2 Q 3 Q 4 Q 5 Q 6 Q 7 Total MM 6 2 4 0 8 4 6 70 Q. Write a

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19

CIS 121 Data Structures and Algorithms with Java Spring Stacks, Queues, and Heaps Monday, February 18 / Tuesday, February 19 CIS Data Structures ad Algorithms with Java Sprig 09 Stacks, Queues, ad Heaps Moday, February 8 / Tuesday, February 9 Stacks ad Queues Recall the stack ad queue ADTs (abstract data types from lecture.

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13 CIS Data Structures ad Algorithms with Java Sprig 08 Stacks ad Queues Moday, February / Tuesday, February Learig Goals Durig this lab, you will: Review stacks ad queues. Lear amortized ruig time aalysis

More information

. Written in factored form it is easy to see that the roots are 2, 2, i,

. Written in factored form it is easy to see that the roots are 2, 2, i, CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or

More information

Programming with Shared Memory PART II. HPC Spring 2017 Prof. Robert van Engelen

Programming with Shared Memory PART II. HPC Spring 2017 Prof. Robert van Engelen Programmig with Shared Memory PART II HPC Sprig 2017 Prof. Robert va Egele Overview Sequetial cosistecy Parallel programmig costructs Depedece aalysis OpeMP Autoparallelizatio Further readig HPC Sprig

More information

Chapter 4 The Datapath

Chapter 4 The Datapath The Ageda Chapter 4 The Datapath Based o slides McGraw-Hill Additioal material 24/25/26 Lewis/Marti Additioal material 28 Roth Additioal material 2 Taylor Additioal material 2 Farmer Tae the elemets that

More information

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved. Chapter 11 Frieds, Overloaded Operators, ad Arrays i Classes Copyright 2014 Pearso Addiso-Wesley. All rights reserved. Overview 11.1 Fried Fuctios 11.2 Overloadig Operators 11.3 Arrays ad Classes 11.4

More information

Inductive Definition to Recursive Function

Inductive Definition to Recursive Function PDS: CS 11002 Computer Sc & Egg: IIT Kharagpur 1 Iductive Defiitio to Recursive Fuctio PDS: CS 11002 Computer Sc & Egg: IIT Kharagpur 2 Factorial Fuctio Cosider the followig recursive defiitio of the factorial

More information

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1 COSC 1P03 Ch 7 Recursio Itroductio to Data Structures 8.1 COSC 1P03 Recursio Recursio I Mathematics factorial Fiboacci umbers defie ifiite set with fiite defiitio I Computer Sciece sytax rules fiite defiitio,

More information

Reversible Realization of Quaternary Decoder, Multiplexer, and Demultiplexer Circuits

Reversible Realization of Quaternary Decoder, Multiplexer, and Demultiplexer Circuits Egieerig Letters, :, EL Reversible Realizatio of Quaterary Decoder, Multiplexer, ad Demultiplexer Circuits Mozammel H.. Kha, Member, ENG bstract quaterary reversible circuit is more compact tha the correspodig

More information

EE260: Digital Design, Spring /16/18. n Example: m 0 (=x 1 x 2 ) is adjacent to m 1 (=x 1 x 2 ) and m 2 (=x 1 x 2 ) but NOT m 3 (=x 1 x 2 )

EE260: Digital Design, Spring /16/18. n Example: m 0 (=x 1 x 2 ) is adjacent to m 1 (=x 1 x 2 ) and m 2 (=x 1 x 2 ) but NOT m 3 (=x 1 x 2 ) EE26: Digital Desig, Sprig 28 3/6/8 EE 26: Itroductio to Digital Desig Combiatioal Datapath Yao Zheg Departmet of Electrical Egieerig Uiversity of Hawaiʻi at Māoa Combiatioal Logic Blocks Multiplexer Ecoders/Decoders

More information

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design

COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Interface. Chapter 4. The Processor. Part A Datapath Design COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Chapter The Processor Part A path Desig Itroductio CPU performace factors Istructio cout Determied by ISA ad compiler. CPI ad

More information

Appendix D. Controller Implementation

Appendix D. Controller Implementation COMPUTER ORGANIZATION AND DESIGN The Hardware/Software Iterface 5 th Editio Appedix D Cotroller Implemetatio Cotroller Implemetatios Combiatioal logic (sigle-cycle); Fiite state machie (multi-cycle, pipelied);

More information

Threads and Concurrency in Java: Part 1

Threads and Concurrency in Java: Part 1 Cocurrecy Threads ad Cocurrecy i Java: Part 1 What every computer egieer eeds to kow about cocurrecy: Cocurrecy is to utraied programmers as matches are to small childre. It is all too easy to get bured.

More information

Matrix representation of a solution of a combinatorial problem of the group theory

Matrix representation of a solution of a combinatorial problem of the group theory Matrix represetatio of a solutio of a combiatorial problem of the group theory Krasimir Yordzhev, Lilyaa Totia Faculty of Mathematics ad Natural Scieces South-West Uiversity 66 Iva Mihailov Str, 2700 Blagoevgrad,

More information

Threads and Concurrency in Java: Part 1

Threads and Concurrency in Java: Part 1 Threads ad Cocurrecy i Java: Part 1 1 Cocurrecy What every computer egieer eeds to kow about cocurrecy: Cocurrecy is to utraied programmers as matches are to small childre. It is all too easy to get bured.

More information

Outline. Applications of FFT in Communications. Fundamental FFT Algorithms. FFT Circuit Design Architectures. Conclusions

Outline. Applications of FFT in Communications. Fundamental FFT Algorithms. FFT Circuit Design Architectures. Conclusions FFT Circuit Desig Outlie Applicatios of FFT i Commuicatios Fudametal FFT Algorithms FFT Circuit Desig Architectures Coclusios DAB Receiver Tuer OFDM Demodulator Chael Decoder Mpeg Audio Decoder 56/5/ 4/48

More information

Example: Shifter. Cascaded Combinational Shifter. Bluespec-2: Types. Asynchronous pipeline with FIFOs (regs with interlocks)

Example: Shifter. Cascaded Combinational Shifter. Bluespec-2: Types. Asynchronous pipeline with FIFOs (regs with interlocks) Bluespec-2: Types Arvid Computer Sciece & Artificial Itelligece Lab Massachusetts Istitute of Techology Eample: Shifter Goal: implemet: y = shift (,s) where y is shifted by s positios. Suppose s is a 3-bit

More information

Solutions to Final COMS W4115 Programming Languages and Translators Monday, May 4, :10-5:25pm, 309 Havemeyer

Solutions to Final COMS W4115 Programming Languages and Translators Monday, May 4, :10-5:25pm, 309 Havemeyer Departmet of Computer ciece Columbia Uiversity olutios to Fial COM W45 Programmig Laguages ad Traslators Moday, May 4, 2009 4:0-5:25pm, 309 Havemeyer Closed book, o aids. Do questios 5. Each questio is

More information

EE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering

EE University of Minnesota. Midterm Exam #1. Prof. Matthew O'Keefe TA: Eric Seppanen. Department of Electrical and Computer Engineering EE 4363 1 Uiversity of Miesota Midterm Exam #1 Prof. Matthew O'Keefe TA: Eric Seppae Departmet of Electrical ad Computer Egieerig Uiversity of Miesota Twi Cities Campus EE 4363 Itroductio to Microprocessors

More information

Today s objectives. CSE401: Introduction to Compiler Construction. What is a compiler? Administrative Details. Why study compilers?

Today s objectives. CSE401: Introduction to Compiler Construction. What is a compiler? Administrative Details. Why study compilers? CSE401: Itroductio to Compiler Costructio Larry Ruzzo Sprig 2004 Today s objectives Admiistrative details Defie compilers ad why we study them Defie the high-level structure of compilers Associate specific

More information

How do we evaluate algorithms?

How do we evaluate algorithms? F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:

More information

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis Itro to Algorithm Aalysis Aalysis Metrics Slides. Table of Cotets. Aalysis Metrics 3. Exact Aalysis Rules 4. Simple Summatio 5. Summatio Formulas 6. Order of Magitude 7. Big-O otatio 8. Big-O Theorems

More information

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 1 Itroductio to Computers ad C++ Programmig Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 1.1 Computer Systems 1.2 Programmig ad Problem Solvig 1.3 Itroductio to C++ 1.4 Testig

More information

Computer Science Foundation Exam. August 12, Computer Science. Section 1A. No Calculators! KEY. Solutions and Grading Criteria.

Computer Science Foundation Exam. August 12, Computer Science. Section 1A. No Calculators! KEY. Solutions and Grading Criteria. Computer Sciece Foudatio Exam August, 005 Computer Sciece Sectio A No Calculators! Name: SSN: KEY Solutios ad Gradig Criteria Score: 50 I this sectio of the exam, there are four (4) problems. You must

More information

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe

Copyright 2016 Ramez Elmasri and Shamkant B. Navathe Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe CHAPTER 19 Query Optimizatio Copyright 2016 Ramez Elmasri ad Shamkat B. Navathe Itroductio Query optimizatio Coducted by a query optimizer i a DBMS Goal:

More information

Data diverse software fault tolerance techniques

Data diverse software fault tolerance techniques Data diverse software fault tolerace techiques Complemets desig diversity by compesatig for desig diversity s s limitatios Ivolves obtaiig a related set of poits i the program data space, executig the

More information

CS 11 C track: lecture 1

CS 11 C track: lecture 1 CS 11 C track: lecture 1 Prelimiaries Need a CMS cluster accout http://acctreq.cms.caltech.edu/cgi-bi/request.cgi Need to kow UNIX IMSS tutorial liked from track home page Track home page: http://courses.cms.caltech.edu/courses/cs11/material

More information

End Semester Examination CSE, III Yr. (I Sem), 30002: Computer Organization

End Semester Examination CSE, III Yr. (I Sem), 30002: Computer Organization Ed Semester Examiatio 2013-14 CSE, III Yr. (I Sem), 30002: Computer Orgaizatio Istructios: GROUP -A 1. Write the questio paper group (A, B, C, D), o frot page top of aswer book, as per what is metioed

More information

CMPT 125 Assignment 2 Solutions

CMPT 125 Assignment 2 Solutions CMPT 25 Assigmet 2 Solutios Questio (20 marks total) a) Let s cosider a iteger array of size 0. (0 marks, each part is 2 marks) it a[0]; I. How would you assig a poiter, called pa, to store the address

More information

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis

More information

COP4020 Programming Languages. Functional Programming Prof. Robert van Engelen

COP4020 Programming Languages. Functional Programming Prof. Robert van Engelen COP4020 Programmig Laguages Fuctioal Programmig Prof. Robert va Egele Overview What is fuctioal programmig? Historical origis of fuctioal programmig Fuctioal programmig today Cocepts of fuctioal programmig

More information

COP4020 Programming Languages. Compilers and Interpreters Prof. Robert van Engelen

COP4020 Programming Languages. Compilers and Interpreters Prof. Robert van Engelen COP4020 mig Laguages Compilers ad Iterpreters Prof. Robert va Egele Overview Commo compiler ad iterpreter cofiguratios Virtual machies Itegrated developmet eviromets Compiler phases Lexical aalysis Sytax

More information

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 9 Poiters ad Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 9.1 Poiters 9.2 Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Slide 9-3

More information

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 10. Defining Classes. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 10 Defiig Classes Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 10.1 Structures 10.2 Classes 10.3 Abstract Data Types 10.4 Itroductio to Iheritace Copyright 2015 Pearso Educatio,

More information

Module 8-7: Pascal s Triangle and the Binomial Theorem

Module 8-7: Pascal s Triangle and the Binomial Theorem Module 8-7: Pascal s Triagle ad the Biomial Theorem Gregory V. Bard April 5, 017 A Note about Notatio Just to recall, all of the followig mea the same thig: ( 7 7C 4 C4 7 7C4 5 4 ad they are (all proouced

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 1 Computers ad Programs 1 Objectives To uderstad the respective roles of hardware ad software i a computig system. To lear what computer scietists

More information

CS 111: Program Design I Lecture # 7: First Loop, Web Crawler, Functions

CS 111: Program Design I Lecture # 7: First Loop, Web Crawler, Functions CS 111: Program Desig I Lecture # 7: First Loop, Web Crawler, Fuctios Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago September 18, 2018 What will this prit? x = 5 if x == 3: prit("hi!")

More information

1. SWITCHING FUNDAMENTALS

1. SWITCHING FUNDAMENTALS . SWITCING FUNDMENTLS Switchig is the provisio of a o-demad coectio betwee two ed poits. Two distict switchig techiques are employed i commuicatio etwors-- circuit switchig ad pacet switchig. Circuit switchig

More information

The following algorithms have been tested as a method of converting an I.F. from 16 to 512 MHz to 31 real 16 MHz USB channels:

The following algorithms have been tested as a method of converting an I.F. from 16 to 512 MHz to 31 real 16 MHz USB channels: DBE Memo#1 MARK 5 MEMO #18 MASSACHUSETTS INSTITUTE OF TECHNOLOGY HAYSTACK OBSERVATORY WESTFORD, MASSACHUSETTS 1886 November 19, 24 Telephoe: 978-692-4764 Fax: 781-981-59 To: From: Mark 5 Developmet Group

More information

A graphical view of big-o notation. c*g(n) f(n) f(n) = O(g(n))

A graphical view of big-o notation. c*g(n) f(n) f(n) = O(g(n)) ca see that time required to search/sort grows with size of We How do space/time eeds of program grow with iput size? iput. time: cout umber of operatios as fuctio of iput Executio size operatio Assigmet:

More information

The isoperimetric problem on the hypercube

The isoperimetric problem on the hypercube The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose

More information

EE123 Digital Signal Processing

EE123 Digital Signal Processing Last Time EE Digital Sigal Processig Lecture 7 Block Covolutio, Overlap ad Add, FFT Discrete Fourier Trasform Properties of the Liear covolutio through circular Today Liear covolutio with Overlap ad add

More information

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs CHAPTER IV: GRAPH THEORY Sectio : Itroductio to Graphs Sice this class is called Number-Theoretic ad Discrete Structures, it would be a crime to oly focus o umber theory regardless how woderful those topics

More information

Lecture 5. Counting Sort / Radix Sort

Lecture 5. Counting Sort / Radix Sort Lecture 5. Coutig Sort / Radix Sort T. H. Corme, C. E. Leiserso ad R. L. Rivest Itroductio to Algorithms, 3rd Editio, MIT Press, 2009 Sugkyukwa Uiversity Hyuseug Choo choo@skku.edu Copyright 2000-2018

More information

Lecture 2. RTL Design Methodology. Transition from Pseudocode & Interface to a Corresponding Block Diagram

Lecture 2. RTL Design Methodology. Transition from Pseudocode & Interface to a Corresponding Block Diagram Lecture 2 RTL Desig Methodology Trasitio from Pseudocode & Iterface to a Correspodig Block Diagram Structure of a Typical Digital Data Iputs Datapath (Executio Uit) Data Outputs System Cotrol Sigals Status

More information

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein 068.670 Subliear Time Algorithms November, 0 Lecture 6 Lecturer: Roitt Rubifeld Scribes: Che Ziv, Eliav Buchik, Ophir Arie, Joatha Gradstei Lesso overview. Usig the oracle reductio framework for approximatig

More information

CSE 417: Algorithms and Computational Complexity

CSE 417: Algorithms and Computational Complexity Time CSE 47: Algorithms ad Computatioal Readig assigmet Read Chapter of The ALGORITHM Desig Maual Aalysis & Sortig Autum 00 Paul Beame aalysis Problem size Worst-case complexity: max # steps algorithm

More information

Improvement of the Orthogonal Code Convolution Capabilities Using FPGA Implementation

Improvement of the Orthogonal Code Convolution Capabilities Using FPGA Implementation Improvemet of the Orthogoal Code Covolutio Capabilities Usig FPGA Implemetatio Naima Kaabouch, Member, IEEE, Apara Dhirde, Member, IEEE, Saleh Faruque, Member, IEEE Departmet of Electrical Egieerig, Uiversity

More information

Elementary Educational Computer

Elementary Educational Computer Chapter 5 Elemetary Educatioal Computer. Geeral structure of the Elemetary Educatioal Computer (EEC) The EEC coforms to the 5 uits structure defied by vo Neuma's model (.) All uits are preseted i a simplified

More information

n Some thoughts on software development n The idea of a calculator n Using a grammar n Expression evaluation n Program organization n Analysis

n Some thoughts on software development n The idea of a calculator n Using a grammar n Expression evaluation n Program organization n Analysis Overview Chapter 6 Writig a Program Bjare Stroustrup Some thoughts o software developmet The idea of a calculator Usig a grammar Expressio evaluatio Program orgaizatio www.stroustrup.com/programmig 3 Buildig

More information

From last week. Lecture 5. Outline. Principles of programming languages

From last week. Lecture 5. Outline. Principles of programming languages Priciples of programmig laguages From last week Lecture 5 http://few.vu.l/~silvis/ppl/2007 Natalia Silvis-Cividjia e-mail: silvis@few.vu.l ML has o assigmet. Explai how to access a old bidig? Is & for

More information

Module Instantiation. Finite State Machines. Two Types of FSMs. Finite State Machines. Given submodule mux32two: Instantiation of mux32two

Module Instantiation. Finite State Machines. Two Types of FSMs. Finite State Machines. Given submodule mux32two: Instantiation of mux32two Give submodule mux32two: 2-to- MUX module mux32two (iput [3:] i,i, iput sel, output [3:] out); Module Istatiatio Fiite Machies esig methodology for sequetial logic -- idetify distict s -- create trasitio

More information

COP4020 Programming Languages. Subroutines and Parameter Passing Prof. Robert van Engelen

COP4020 Programming Languages. Subroutines and Parameter Passing Prof. Robert van Engelen COP4020 Programmig Laguages Subrouties ad Parameter Passig Prof. Robert va Egele Overview Parameter passig modes Subroutie closures as parameters Special-purpose parameters Fuctio returs COP4020 Fall 2016

More information

Overview. Chapter 18 Vectors and Arrays. Reminder. vector. Bjarne Stroustrup

Overview. Chapter 18 Vectors and Arrays. Reminder. vector. Bjarne Stroustrup Chapter 18 Vectors ad Arrays Bjare Stroustrup Vector revisited How are they implemeted? Poiters ad free store Destructors Iitializatio Copy ad move Arrays Array ad poiter problems Chagig size Templates

More information

A Very Simple Approach for 3-D to 2-D Mapping

A Very Simple Approach for 3-D to 2-D Mapping A Very Simple Approach for -D to -D appig Sadipa Dey (1 Ajith Abraham ( Sugata Sayal ( Sadipa Dey (1 Ashi Software Private Limited INFINITY Tower II 10 th Floor Plot No. - 4. Block GP Salt Lake Electroics

More information

Examples and Applications of Binary Search

Examples and Applications of Binary Search Toy Gog ITEE Uiersity of Queeslad I the secod lecture last week we studied the biary search algorithm that soles the problem of determiig if a particular alue appears i a sorted list of iteger or ot. We

More information

BOOLEAN MATHEMATICS: GENERAL THEORY

BOOLEAN MATHEMATICS: GENERAL THEORY CHAPTER 3 BOOLEAN MATHEMATICS: GENERAL THEORY 3.1 ISOMORPHIC PROPERTIES The ame Boolea Arithmetic was chose because it was discovered that literal Boolea Algebra could have a isomorphic umerical aspect.

More information

What are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs

What are we going to learn? CSC Data Structures Analysis of Algorithms. Overview. Algorithm, and Inputs What are we goig to lear? CSC316-003 Data Structures Aalysis of Algorithms Computer Sciece North Carolia State Uiversity Need to say that some algorithms are better tha others Criteria for evaluatio Structure

More information

Software development of components for complex signal analysis on the example of adaptive recursive estimation methods.

Software development of components for complex signal analysis on the example of adaptive recursive estimation methods. Software developmet of compoets for complex sigal aalysis o the example of adaptive recursive estimatio methods. SIMON BOYMANN, RALPH MASCHOTTA, SILKE LEHMANN, DUNJA STEUER Istitute of Biomedical Egieerig

More information

1.2 Binomial Coefficients and Subsets

1.2 Binomial Coefficients and Subsets 1.2. BINOMIAL COEFFICIENTS AND SUBSETS 13 1.2 Biomial Coefficiets ad Subsets 1.2-1 The loop below is part of a program to determie the umber of triagles formed by poits i the plae. for i =1 to for j =

More information

n We have discussed classes in previous lectures n Here, we discuss design of classes n Library design considerations

n We have discussed classes in previous lectures n Here, we discuss design of classes n Library design considerations Chapter 14 Graph class desig Bjare Stroustrup Abstract We have discussed classes i previous lectures Here, we discuss desig of classes Library desig cosideratios Class hierarchies (object-orieted programmig)

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 6 Defiig Fuctios Pytho Programmig, 2/e 1 Objectives To uderstad why programmers divide programs up ito sets of cooperatig fuctios. To be able to

More information

COMP Parallel Computing. PRAM (1): The PRAM model and complexity measures

COMP Parallel Computing. PRAM (1): The PRAM model and complexity measures COMP 633 - Parallel Computig Lecture 2 August 24, 2017 : The PRAM model ad complexity measures 1 First class summary This course is about parallel computig to achieve high-er performace o idividual problems

More information

Last class. n Scheme. n Equality testing. n eq? vs. equal? n Higher-order functions. n map, foldr, foldl. n Tail recursion

Last class. n Scheme. n Equality testing. n eq? vs. equal? n Higher-order functions. n map, foldr, foldl. n Tail recursion Aoucemets HW6 due today HW7 is out A team assigmet Submitty page will be up toight Fuctioal correctess: 75%, Commets : 25% Last class Equality testig eq? vs. equal? Higher-order fuctios map, foldr, foldl

More information

Outline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis

Outline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis Outlie ad Readig Aalysis of Algorithms Iput Algorithm Output Ruig time ( 3.) Pseudo-code ( 3.2) Coutig primitive operatios ( 3.3-3.) Asymptotic otatio ( 3.6) Asymptotic aalysis ( 3.7) Case study Aalysis

More information

Analysis of Algorithms

Analysis of Algorithms Aalysis of Algorithms Ruig Time of a algorithm Ruig Time Upper Bouds Lower Bouds Examples Mathematical facts Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite

More information

GPUMP: a Multiple-Precision Integer Library for GPUs

GPUMP: a Multiple-Precision Integer Library for GPUs GPUMP: a Multiple-Precisio Iteger Library for GPUs Kaiyog Zhao ad Xiaowe Chu Departmet of Computer Sciece, Hog Kog Baptist Uiversity Hog Kog, P. R. Chia Email: {kyzhao, chxw}@comp.hkbu.edu.hk Abstract

More information

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS)

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS) CSC165H1, Witer 018 Learig Objectives By the ed of this worksheet, you will: Aalyse the ruig time of fuctios cotaiig ested loops. 1. Nested loop variatios. Each of the followig fuctios takes as iput a

More information

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only

Bezier curves. Figure 2 shows cubic Bezier curves for various control points. In a Bezier curve, only Edited: Yeh-Liag Hsu (998--; recommeded: Yeh-Liag Hsu (--9; last updated: Yeh-Liag Hsu (9--7. Note: This is the course material for ME55 Geometric modelig ad computer graphics, Yua Ze Uiversity. art of

More information

The number n of subintervals times the length h of subintervals gives length of interval (b-a).

The number n of subintervals times the length h of subintervals gives length of interval (b-a). Simulator with MadMath Kit: Riema Sums (Teacher s pages) I your kit: 1. GeoGebra file: Ready-to-use projector sized simulator: RiemaSumMM.ggb 2. RiemaSumMM.pdf (this file) ad RiemaSumMMEd.pdf (educator's

More information

APPLICATION NOTE PACE1750AE BUILT-IN FUNCTIONS

APPLICATION NOTE PACE1750AE BUILT-IN FUNCTIONS APPLICATION NOTE PACE175AE BUILT-IN UNCTIONS About This Note This applicatio brief is iteded to explai ad demostrate the use of the special fuctios that are built ito the PACE175AE processor. These powerful

More information

Baan Tools User Management

Baan Tools User Management Baa Tools User Maagemet Module Procedure UP008A US Documetiformatio Documet Documet code : UP008A US Documet group : User Documetatio Documet title : User Maagemet Applicatio/Package : Baa Tools Editio

More information

CMSC Computer Architecture Lecture 3: ISA and Introduction to Microarchitecture. Prof. Yanjing Li University of Chicago

CMSC Computer Architecture Lecture 3: ISA and Introduction to Microarchitecture. Prof. Yanjing Li University of Chicago CMSC 22200 Computer Architecture Lecture 3: ISA ad Itroductio to Microarchitecture Prof. Yajig Li Uiversity of Chicago Lecture Outlie ISA uarch (hardware implemetatio of a ISA) Logic desig basics Sigle-cycle

More information

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments Ruig Time Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects. The

More information

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup Note:

Abstract. Chapter 4 Computation. Overview 8/13/18. Bjarne Stroustrup   Note: Chapter 4 Computatio Bjare Stroustrup www.stroustrup.com/programmig Abstract Today, I ll preset the basics of computatio. I particular, we ll discuss expressios, how to iterate over a series of values

More information

Fast Fourier Transform (FFT)

Fast Fourier Transform (FFT) IC Desig Lab. Fast Fourier Trasfor FFT, - e e π π G Twiddle Factor : e π Radi- DIT FFT: IC Desig Lab. π cos si π Syetry Property Periodicity Property u u u * Cougate Syetric Property a a b b a b b a b

More information