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  • DFT = fast way to multiply two univariate polynomials
    FFT reference: Runge-König (1924), Cooley-Tukey (1965) rediscovered FFT and popularized it. Discovered by Gauss at age of 28, two years before Fourier's paper on Fourier transforms!
    medical imaging = CAT scan, MR
    astrophysicists want N = 64 gigapoints to search for pulsars; seismologists want N = 10 gigapoints to predict earthquakes
  • Appel designed his fast multipole method for senior thesis in Physics at Princeton! (actually can be made linear)
    physicists want N = # atoms in the universe
  • assumes no integer overflow
  • Run ThreeSum.java in class from the command-line and wait for it to finish, perhaps up to N = 4096.
    Ask students to make predictions.
  • log-log plot identifies power law relationships
  • Very accurate predictions.
    4x input size -> 64x increase in running time for cubic algorithm
  • put more emphasis on ratios with larger N since lots of noise contained in data for smaller N
  • if connected to Internet, some hacker is using your computer to send spam, steal your money, etc.
    Good news: compared to other scientists, running computational experiments is easy. Don't need to kill cows, blow up islands.
  • most famous modeling by Knuth - detailed results to accurate predict running time
    Knuth's idea seems obvious in hindsight, but in the 1960s, programmers said it would never be possible to analyze the running time of a computer program since they are too complicated. Knuth's TAOCP is now a classic treatise on the subject.
  • consider putting in instruction counts for N = 10000
  • N^3 / 6 arises because N choose 3 is the number of triples i, j, k
  • this partially explains reason why small set of functions
    arises from straightforward composition of loops, conditionals, functions
  • didn't put column for cost - in 1970s, machine cost millions of $$$
    Ex. Population of NYC was “millions” in 1970s; still is
    Ex. Customers lost patience waiting “minutes” in 1970s; still do
  • Note: not an issue for 3-sum since best, average, and worst are all almost the same.
  • dependence on input implied ????
  • table contains typical memory requirements for primitive types
  • 2d array is represented as an array of arrays: e.g., for double[][], there is one array with N references to arrays (4N + 16) and N double[] arrays (N (8N + 16)) for a total of 8N^2 + 20N + 16 bytes.
  • A. ~ 4N^2
  • What to do in last case? Need better algorithm that uses less memory.
  • Mathematics might be difficult.
    It is known that properties of singularities of functions in the complex plane play a role in analysis of many algorithms



    Leading term might not be good enough?
    Typical programs have bottleneck in one function.
    Debugging tools are available to identify bottlenecks.



    Timing may be flawed.
    Different results on different computers.
    Different results on same computer at different times.

Transcript

  • 1. Analysis of Algorithms ‣ estimating running time ‣ mathematical analysis ‣ order-of-growth hypotheses ‣ input models ‣ measuring space Reference: Algorithms in Java, Chapter 2 Intro to Programming in Java, Section 4.1 Except as otherwise noted, the content of this presentation http://www.cs.princeton.edu/algs4 is licensed under the Creative Commons Attribution 2.5 License. Algorithms in Java, 4th Edition · Robert Sedgewick and Kevin Wayne · Copyright © 2008 · September 2, 2009 2:46:54 AM
  • 2. Running time “ As soon as an Analytic Engine exists, it will necessarily guide the future course of the science. Whenever any result is sought by its aid, the question how many times do you have to turn the crank? Charles Babbage (1864) Analytic Engine 2
  • 3. Reasons to analyze algorithms Predict performance. this course (COS 226) Compare algorithms. Provide guarantees. Understand theoretical basis. theory of algorithms (COS 423) client gets poor performance because programmer did not understand performance characteristics Primary practical reason: avoid performance bugs. 3
  • 4. Some algorithmic successes Discrete Fourier transform. • Break down waveform of N samples into periodic components. • Applications: DVD, JPEG, MRI, astrophysics, …. • Brute force: N2 steps. Freidrich Gauss • FFT algorithm: N log N steps, enables new technology. 1805 time quadratic 64T 32T 16T linearithmic 8T linear size 1K 2K 4K 8K Linear, linearithmic, and quadratic 4
  • 5. Some algorithmic successes N-body Simulation. • Simulate gravitational interactions among N bodies. • Brute force: N2 steps. • Barnes-Hut: N log N steps, enables new research. Andrew Appel PU '81 time quadratic 64T 32T 16T linearithmic 8T linear size 1K 2K 4K 8K Linear, linearithmic, and quadratic 5
  • 6. ‣ estimating running time ‣ mathematical analysis ‣ order-of-growth hypotheses ‣ input models ‣ measuring space 6
  • 7. Scientific analysis of algorithms A framework for predicting performance and comparing algorithms. Scientific method. • Observe some feature of the universe. • Hypothesize a model that is consistent with observation. • Predict events using the hypothesis. • Verify the predictions by making further observations. • Validate by repeating until the hypothesis and observations agree. Principles. • Experiments must be reproducible. • Hypotheses must be falsifiable. Universe = computer itself. 7
  • 8. Experimental algorithmics Every time you run a program you are doing an experiment! Why is my program so slow ? First step. Debug your program! Second step. Choose input model for experiments. Third step. Run and time the program for problems of increasing size. 8
  • 9. Example: 3-sum 3-sum. Given N integers, find all triples that sum to exactly zero. Application. Deeply related to problems in computational geometry. % more 8ints.txt 30 -30 -20 -10 40 0 10 5 % java ThreeSum < 8ints.txt 4 30 -30 0 30 -20 -10 -30 -10 40 -10 0 10 9
  • 10. 3-sum: brute-force algorithm public class ThreeSum { public static int count(long[] a) { int N = a.length; int cnt = 0; for (int i = 0; i < N; i++) for (int j = i+1; j < N; j++) for (int k = j+1; k < N; k++) check each triple if (a[i] + a[j] + a[k] == 0) cnt++; return cnt; } public static void main(String[] args) { int[] a = StdArrayIO.readLong1D(); StdOut.println(count(a)); } } 10
  • 11. Measuring the running time Q. How to time a program? A. Manual. 11
  • 12. Measuring the running time Q. How to time a program? A. Automatic. Stopwatch stopwatch = new Stopwatch(); ThreeSum.count(a); double time = stopwatch.elapsedTime(); StdOut.println("Running time: " + time + " seconds"); client code public class Stopwatch { private final long start = System.currentTimeMillis(); public double elapsedTime() { long now = System.currentTimeMillis(); return (now - start) / 1000.0; } } implementation 12
  • 13. 3-sum: initial observations Data analysis. Observe and plot running time as a function of input size N. N time (seconds) † 1024 0.26 2048 2.16 4096 17.18 8192 137.76 † Running Linux on Sun-Fire-X4100 13
  • 14. Empirical analysis Log-log plot. Plot running time vs. input size N on log-log scale. slope = 3 power law Regression. Fit straight line through data points: c N a. slope Hypothesis. Running time grows cubically with input size: c N 3. 14
  • 15. Prediction and verification Hypothesis. 2.5 × 10 –10 × N 3 seconds for input of size N. Prediction. 17.18 seconds for N = 4,096. Observations. N time (seconds) 4096 17.18 4096 17.15 agrees 4096 17.17 Prediction. 1100 seconds for N = 16,384. Observation. N time (seconds) agrees 16384 1118.86 15
  • 16. Doubling hypothesis Q. What is effect on the running time of doubling the size of the input? N time (seconds) † ratio 512 0.03 - 1024 0.26 7.88 2048 2.16 8.43 4096 17.18 7.96 8192 137.76 7.96 numbers increases running time increases by a factor of 2 by a factor of 8 lg of ratio is exponent in power law (lg 7.96 ≈ 3) Bottom line. Quick way to formulate a power law hypothesis. 16
  • 17. Experimental algorithmics Many obvious factors affect running time: • Machine. • Compiler. • Algorithm. • Input data. More factors (not so obvious): • Caching. • Garbage collection. • Just-in-time compilation. • CPU use by other applications. Bad news. It is often difficult to get precise measurements. Good news. Easier than other sciences. e.g., can run huge number of experiments 17
  • 18. ‣ estimating running time ‣ mathematical analysis ‣ order-of-growth hypotheses ‣ input models ‣ measuring space 18
  • 19. Mathematical models for running time Total running time: sum of cost × frequency for all operations. • Need to analyze program to determine set of operations. • Cost depends on machine, compiler. • Frequency depends on algorithm, input data. Donald Knuth 1974 Turing Award In principle, accurate mathematical models are available. 19
  • 20. Cost of basic operations operation example nanoseconds † integer add a + b 2.1 integer multiply a * b 2.4 integer divide a / b 5.4 floating point add a + b 4.6 floating point multiply a * b 4.2 floating point divide a / b 13.5 sine Math.sin(theta) 91.3 arctangent Math.atan2(y, x) 129.0 ... ... ... † Running OS X on Macbook Pro 2.2GHz with 2GB RAM 20
  • 21. Cost of basic operations operation example nanoseconds † variable declaration int a c1 assignment statement a = b c2 integer compare a < b c3 array element access a[i] c4 array length a.length c5 1D array allocation new int[N] c6 N 2D array allocation new int[N][N] c7 N 2 string length s.length() c8 substring extraction s.substring(N/2, N) c9 string concatenation s + t c10 N Novice mistake. Abusive string concatenation. 21
  • 22. Example: 1-sum Q. How many instructions as a function of N? int count = 0; for (int i = 0; i < N; i++) if (a[i] == 0) count++; operation frequency variable declaration 2 assignment statement 2 less than comparison N+1 equal to comparison N between N (no zeros) and 2N (all zeros) array access N increment ≤ 2N 22
  • 23. Example: 2-sum Q. How many instructions as a function of N? int count = 0; for (int i = 0; i < N; i++) for (int j = i+1; j < N; j++) if (a[i] + a[j] == 0) count++; 1 0 + 1 + 2 + . . . + (N − 1) = N (N − 1) operation frequency 2 N = 2 variable declaration N+2 assignment statement N+2 less than comparison 1/2 (N + 1) (N + 2) equal to comparison 1/2 N (N − 1) tedious to count exactly array access N (N − 1) increment ≤ N2 23
  • 24. Tilde notation • Estimate running time (or memory) as a function of input size N. • Ignore lower order terms. - when N is large, terms are negligible - when N is small, we don't care Ex 1. 6 N 3 + 20 N + 16 ~ 6N3 Ex 2. 6 N 3 + 100 N 4/3 + 56 ~ 6N3 Ex 3. 6 N 3 + 17 N 2 lg N + 7 N ~ 6N3 discard lower-order terms (e.g., N = 1000 6 trillion vs. 169 million) f (N) Technical definition. f(N) ~ g(N) means lim = 1 N→ ∞ g(N) € 24
  • 25. Example: 2-sum Q. How long will it take as a function of N? int count = 0; for (int i = 0; i < N; i++) for (int j = i+1; j < N; j++) if (a[i] + a[j] == 0) count++; "inner loop" operation frequency cost total cost variable declaration ~ N c1 ~ c1 N assignment statement ~ N c2 ~ c2 N less than comparison ~ 1/2 N 2 c3 ~ c3 N 2 equal to comparison ~ 1/2 N 2 array access ~ N2 c4 ~ c4 N 2 increment ≤ N2 c5 ≤ c5 N 2 total ~ cN2 25
  • 26. Example: 3-sum 478 Algorithms and Data Q. How many instructions as a function of N? public class ThreeSum { the leading term of math public static int count(int[] a) { pressions by using a m int N = a.length; device known as the til int cnt = 0; 1 We write f (N) to re for (int i = 0; i < N; i++) quantity that, when divid for (int j = i+1; j < N; j++) N inner for (int k = j+1; k < N; k++) ~N / 2 2 approaches 1 as N grow loop if (a[i] + a[j] + a[k] == 0) ~N / 6 3 write N g (N)N (Nf − 1)(N −to in = (N) 2) 3 3! cnt++; g (N) f (N)1approaches 1 ∼ N 3 6 return cnt; depends on input data With this notation, we } complicated parts of an public static void main(String[] args) that represent small val { int[] a = StdArrayIO.readInt1D(); ample, the if statement int cnt = count(a); is executed N 3/6 tim StdOut.println(cnt); } N (N 1)(N 2)/6 N 3/ } Remark. Focus on instructions in inner loop; ignore everything else!N/3, which certainly, whe Anatomy of a program’s statement execution frequencies N 3/6, approaches 1 as N notation is useful when t ter the leading term are r 26
  • 27. Mathematical models for running time In principle, accurate mathematical models are available. In practice, • Formulas can be complicated. • Advanced mathematics might be required. • Exact models best left for experts. costs (depend on machine, compiler) TN = c1 A + c2 B + c3 C + c4 D + c5 E A = variable declarations B = assignment statements frequencies C = compare (depend on algorithm, input) D = array access E = increment Bottom line. We use approximate models in this course: TN ~ c N3. 27
  • 28. ‣ estimating running time ‣ mathematical analysis ‣ order-of-growth hypotheses ‣ input models ‣ measuring space 28
  • 29. Common order-of-growth hypotheses To determine order-of-growth: • Assume a power law TN ~ c N a. • Estimate exponent a with doubling hypothesis. • Validate with mathematical analysis. N time (seconds) † Ex. ThreeSumDeluxe.java 1,000 0.43 Food for thought. How is it implemented? 2,000 0.53 4,000 1.01 8,000 2.87 16,000 11.00 32,000 44.64 64,000 177.48 observations Caveat. Can't identify logarithmic factors with doubling hypothesis. 29
  • 30. Common order-of-growth hypotheses Linearithmic. We use the term linearithmic to describe programs whose running time for a problem of size N has order of growth N log N. Again, the base of the Good news. the smallrelevant. For example, CouponCollector (PROGRAM 1.4.2) is lin- logarithm is not set of functions earithmic.N, prototypical example 2, mergesort (see NROGRAM 4.2.6). Several im- 1, log The N, N log N, N is N3, and 2P portant problems have natural solutions that are quadratic but clever algorithms suffices to describe order-of-growth of typical algorithms. impor- that are linearithmic. Such algorithms (including mergesort) are critically tant in practice because they enable us to address problem sizes far larger than could be addressed with quadratic solutions. In the next section, we consider a general design technique for developing time growth rate linearithmic algorithms. name T(2N) / T(N) 1024T Quadratic. A 1 constant 1 exponential typical program whose c tic cubi ea c i hm dra 512T running time has order of growth N 2 r rit qua log N logarithmic ~1 ea lin lin has two nested for loops, used for some calculation involving all pairs linear ele- N of N 2 64T ments. The force update double loop in NBody (PROGRAM 3.4.2) is a linearithmicof N log N prototype ~2 the programs in this classification, as is the elementaryN2 quadratic sorting algorithm Inser- 4 8T tion (PROGRAM 4.2.4). 4T N 3 cubic 8 2T Cubic. Our example for this section, logarithmic N 2 exponential T(N) ThreeSum, is cubic (its running time has T constant order of growth N 3) because it has three size 1K 2K 4K 8K 1024K nested for loops, to process all triples of N elements. The running time of matrix factor for Orders of growth (log-log plot) multiplication, as implemented in SEC- doubling hypothesis TION 1.4 has order of growth M 3 to mul- tiply two M-by-M matrices, so the basic matrix multiplication algorithm is often considered to be cubic. However, the size of the input (the number of entries in the 30
  • 31. Common order-of-growth hypotheses growth name typical code framework description example rate 1 constant a = b + c; statement add two numbers while (N > 1) log N logarithmic { N = N / 2; ... } divide in half binary search for (int i = 0; i < N; i++) N linear { ... } loop find the maximum divide N log N linearithmic [see lecture 5] mergesort and conquer for (int i = 0; i < N; i++) N2 quadratic for (int j = 0; j < N; j++) double loop check all pairs { ... } for (int i = 0; i < N; i++) for (int j = 0; j < N; j++) N3 cubic for (int k = 0; k < N; k++) triple loop check all triples { ... } exhaustive check all 2N exponential [see lecture 24] search possibilities 31
  • 32. Practical implications of order-of-growth Q. How long to process millions of inputs? Ex. Population of NYC was "millions" in 1970s; still is. seconds equivalent 1 1 second Q. How many inputs can be processed in minutes? 10 10 seconds Ex. Customers lost patience waiting "minutes" in 1970s; 102 1.7 minutes they still do. 103 17 minutes 104 2.8 hours 105 1.1 days For back-of-envelope calculations, assume: 106 1.6 weeks 107 3.8 months processor instructions decade speed per second 108 3.1 years 1970s 1 MHz 106 109 3.1 decades 1980s 10 MHz 107 1010 3.1 centuries 1990s 100 MHz 108 … forever 2000s 1 GHz 109 1017 age of universe 32
  • 33. Practical implications of order-of-growth problem size solvable in minutes time to process millions of inputs growth rate 1970s 1980s 1990s 2000s 1970s 1980s 1990s 2000s 1 any any any any instant instant instant instant log N any any any any instant instant instant instant tens of hundreds of N millions billions minutes seconds second instant millions millions hundreds of hundreds of tens of N log N millions millions hour minutes seconds thousands millions seconds tens of N2 hundreds thousand thousands decades years months weeks thousands N3 hundred hundreds thousand thousands never never never millennia 33
  • 34. Practical implications of order-of-growth effect on a program that runs for a few seconds growth name description rate time for 100x size for 100x more data faster computer 1 constant independent of input size - - log N logarithmic nearly independent of input size - - N linear optimal for N inputs a few minutes 100x N log N linearithmic nearly optimal for N inputs a few minutes 100x N2 quadratic not practical for large problems several hours 10x N3 cubic not practical for medium problems several weeks 4-5x 2N exponential useful only for tiny problems forever 1x 34
  • 35. ‣ estimating running time ‣ mathematical analysis ‣ order-of-growth hypotheses ‣ input models ‣ measuring space 35
  • 36. Types of analyses Best case. Running time determined by easiest inputs. Ex. N-1 compares to insertion sort N elements in ascending order. Worst case. Running time guarantee for all inputs. Ex. No more than ½N2 compares to insertion sort any N elements. Average case. Expected running time for "random" input. Ex. ~ ¼ N2 compares on average to insertion sort N random elements. 36
  • 37. Commonly-used notations notation provides example shorthand for used to 10 N 2 provide Tilde leading term ~ 10 N 2 10 N 2 + 22 N log N approximate model 10 N 2 + 2 N +37 N2 asymptotic classify Big Theta Θ(N 2) 9000 N 2 growth rate algorithms 5 N 2 + 22 N log N + 3N N2 develop Big Oh Θ(N 2) and smaller O(N 2) 100 N upper bounds 22 N log N + 3 N 9000 N 2 develop Big Omega Θ(N 2) and larger Ω(N 2) N5 lower bounds N 3 + 22 N log N + 3 N
  • 38. Commonly-used notations Ex 1. Our brute-force 3-sum algorithm takes Θ(N 3) time. Ex 2. Conjecture: worst-case running time for any 3-sum algorithm is Ω(N 2). Ex 3. Insertion sort uses O(N 2) compares to sort any array of N elements; it uses ~ N compares in best case (already sorted) and ~ ½N 2 compares in the worst case (reverse sorted). Ex 4. The worst-case height of a tree created with union find with path compression is Θ(N). Ex 5. The height of a tree created with weighted quick union is O(log N). base of logarithm absorbed by big-Oh 1 loga N = logb N logb a 38
  • 39. Predictions and guarantees Theory of algorithms. Worst-case running time of an algorithm is O(f(N)). Advantages • describes guaranteed performance. time/memory • O-notation absorbs input model. f(N) Challenges values represented • by O(f(N)) Cannot use to predict performance. • Cannot use to compare algorithms. input size 39
  • 40. Predictions and guarantees Experimental algorithmics. Given input model,average-case running time is ~ c f(N). Advantages. time/memory • Can use to predict performance. • Can use to compare algorithms. c f(N) values represented Challenges. by ~ c f(N) • Need to develop accurate input model. • May not provide guarantees. input size 40
  • 41. ‣ estimating running time ‣ mathematical analysis ‣ order-of-growth hypotheses ‣ input models ‣ measuring space 41
  • 42. Typical memory requirements for primitive types in Java Bit. 0 or 1. Byte. 8 bits. Megabyte (MB). 210 bytes ~ 1 million bytes. Gigabyte (GB). 220 bytes ~ 1 billion bytes. type bytes boolean 1 byte 1 char 2 int 4 float 4 long 8 double 8 42
  • 43. Typical memory requirements for arrays in Java Array overhead. 16 bytes on a typical machine. type bytes type bytes char[] 2N + 16 char[][] 2N2 + 20N + 16 int[] 4N + 16 int[][] 4N2 + 20N + 16 double[] 8N + 16 double[][] 8N2 + 20N + 16 one-dimensional arrays two-dimensional arrays Q. What's the biggest double[] array you can store on your computer? typical computer in 2008 has about 1GB memory 43
  • 44. uble instance vari- Typical memory requirements for objects in Java rograms create mil- nformation needed Object overhead. 8 bytes on a typical machine. ansparency). A refer- Reference. 4 bytes on a typical machine. data type contains a ytes for theEach Complex object consumes 24 bytes of memory. Ex 1. reference eded for the object’s am 3.2.1) public class Complex 32 bytes 8 bytes overhead for object Charge { object private double re; 8 bytes overhead uble rx; private double im; 8 bytes uble ry; rx ... double uble q; ry } values 24 bytes q gram 3.2.6) 24 bytes Complex object overhead ouble re; ouble im; re double im values rary) 12 bytes 44
  • 45. object (Program 3.2.6) 24 bytes c class Typical Complex memory requirements for objects in Java object overhead ivate double re; ivate double im; re double Object overhead. im 8 bytes on a typical machine. values Reference. 4 bytes on a typical machine. ct (Java library) 12 bytes ic class Color Ex 2. A String of length N consumes 2N + 40 bytes. object overhead rivate byte r; rivate byte g; r g b a rivate byte b; rivate byte a; public class String byte 8 bytes overhead for object values { private int offset; 4 bytes object (Program 3.3.4) 16 bytes + string + vector private int count; 4 bytes c class Document private int hash; object 4 bytes overhead ivate String id;private char[] value; 4 bytes for reference ivate Vector profile; id ... references (plus 2N + 16 bytes for array) profile } 2N + 40 bytes ect (Java library) 24 bytes + char array c class String object overhead ivate char[] val; ivate int offset; value reference ivate int count; offset ivate int hash; int count values hash 45
  • 46. Example 1 Q. How much memory does this program use as a function of N ? public class RandomWalk { public static void main(String[] args) { int N = Integer.parseInt(args[0]); int[][] count = new int[N][N]; int x = N/2; int y = N/2; for (int i = 0; i < N; i++) { // no new variable declared in loop ... count[x][y]++; } } } 46
  • 47. Example 2 Q. How much memory does this code fragment use as a function of N ? ... int N = Integer.parseInt(args[0]); for (int i = 0; i < N; i++) { int[] a = new int[N]; ... } Remark. Java automatically reclaims memory when it is no longer in use. 47
  • 48. Out of memory Q. What if I run out of memory? % java RandomWalk 10000 Exception in thread "main" java.lang.OutOfMemoryError: Java heap space % java -Xmx 500m RandomWalk 10000 ... % java RandomWalk 30000 Exception in thread "main" java.lang.OutOfMemoryError: Java heap space % java -Xmx 4500m RandomWalk 30000 Invalid maximum heap size: -Xmx4500m The specified size exceeds the maximum representable size. Could not create the Java virtual machine. 48
  • 49. Turning the crank: summary In principle, accurate mathematical models are available. In practice, approximate mathematical models are easily achieved. Timing may be flawed? • Limits on experiments insignificant compared to other sciences. • Mathematics might be difficult? • Only a few functions seem to turn up. • Doubling hypothesis cancels complicated constants. Actual data might not match input model? • Need to understand input to effectively process it. • Approach 1: design for the worst case. • Approach 2: randomize, depend on probabilistic guarantee. 49