test/loops/TSPExchangerTest.java

/*
 * Written by Doug Lea and Bill Scherer with assistance from members
 * of JCP JSR-166 Expert Group and released to the public domain, as
 * explained at http://creativecommons.org/licenses/publicdomain
 */

import java.util.*;
import java.util.concurrent.*;
import java.util.concurrent.atomic.*;
import java.util.concurrent.locks.*;

/**
 * A parallel Traveling Salesperson Problem (TSP) program based on a
 * genetic algorithm using an Exchanger.  A population of chromosomes
 * is distributed among "pools".  The chromosomes represent tours, and
 * their fitness is the total tour length. A Task is associated with
 * each pool.  Each task repeatedly does, for a fixed number of
 * iterations (generations):
 * <ol>
 *   <li> Select a breeder b from the pool
 *   <li> Create a strand of its tour with a random starting point and length
 *   <li> Offer the strand to the exchanger, receiving a strand from 
 *        another pool
 *   <li> Combine b and the received strand using crossing function to 
 *        create new chromosome c.
 *   <li> Replace a chromosome in the pool with c.
 * </ol>
 *
 * See below for more details.
 * <p>
 * 
 */
public class TSPExchangerTest {
    static final int NCPUS = Runtime.getRuntime().availableProcessors();

    static final int DEFAULT_MAX_THREADS  = NCPUS + 6;

    /**
     * The problem size. Each city is a random point. The goal is to
     * find a tour among them with smallest total Euclidean distance.
     */
    static final int DEFAULT_CITIES = 144;

    // Tuning parameters. 

    /**
     * The number of chromosomes per pool. Must be a power of two.
     *
     * Smaller values lead to faster iterations but poorer quality
     * results
     */
    static final int DEFAULT_POOL_SIZE = 32; 

    /**
     * The number of iterations per task. Convergence appears
     * to be roughly proportional to #cities-squared
     */
    static final int DEFAULT_GENERATIONS = DEFAULT_CITIES * DEFAULT_CITIES;

    /**
     * The number of pools. The total population is #pools * poolSize,
     * which should be roughly on the order of #cities-squared
     *
     * Smaller values lead to faster total runs but poorer quality
     * results
     */
    static final int DEFAULT_NPOOLS = DEFAULT_GENERATIONS / DEFAULT_POOL_SIZE;

    /**
     * The minimum length for a random chromosome strand.
     * Must be at least 1.
     */
    static final int MIN_STRAND_LENGTH = 3;

    /**
     * The probablility mask value for creating random strands,
     * that have lengths at least MIN_STRAND_LENGTH, and grow
     * with exposnential decay 2^(-(1/(RANDOM_STRAND_MASK + 1)
     * Must be 1 less than a power of two.
     */
    static final int RANDOM_STRAND_MASK = 7;

    /**
     * Probablility control for selecting breeders.
     * Breeders are selected starting at the best-fitness chromosome,
     * with exponentially decaying probablility
     * 1 / (poolSize >>> BREEDER_DECAY). 
     *
     * Larger values usually cause faster convergence but poorer
     * quality results
     */
    static final int BREEDER_DECAY = 1;

    /**
     * Probablility control for selecting dyers.
     * Dyers are selected starting at the worst-fitness chromosome,
     * with exponentially decaying probablility
     * 1 / (poolSize >>> DYER_DECAY)
     *
     * Larger values usually cause faster convergence but poorer
     * quality results
     */
    static final int DYER_DECAY = 1;

    static final boolean verbose = false;
    static final long SNAPSHOT_RATE = 10000; // in milliseconds

    /**
     * The set of cities. Created once per program run, to
     * make it easier to compare solutions across different runs.
     */
    static CitySet cities; 

    public static void main(String[] args) throws Exception {
        int maxThreads = DEFAULT_MAX_THREADS;
        int nCities = DEFAULT_CITIES;
        int poolSize = DEFAULT_POOL_SIZE;
        int nGen = nCities * nCities;
        int nPools = nCities * nCities / poolSize;

        try {
            int argc = 0;
            while (argc < args.length) {
                String option = args[argc++];
                if (option.equals("-c")) {
                    nCities = Integer.parseInt(args[argc]);
                    nGen = nCities * nCities;
                    nPools = nCities * nCities / poolSize;
                }
                else if (option.equals("-p"))
                    poolSize = Integer.parseInt(args[argc]);
                else if (option.equals("-g"))
                    nGen = Integer.parseInt(args[argc]);
                else if (option.equals("-n"))
                    nPools = Integer.parseInt(args[argc]);
                else
                    maxThreads = Integer.parseInt(option);
                argc++;
            }
        }
        catch (Exception e) {
            reportUsageErrorAndDie();
        }

        System.out.print("TSPExchangerTest");
        System.out.print(" -c " + nCities);
        System.out.print(" -g " + nGen);
        System.out.print(" -p " + poolSize);
        System.out.print(" -n " + nPools);
        System.out.print(" max threads " + maxThreads);
        System.out.println();

        cities = new CitySet(nCities);

        for (int i = 2; i <= maxThreads; i += 2) 
            oneRun(i, nPools, poolSize, nGen);
    }

    static void reportUsageErrorAndDie() {
        System.out.print("usage: TSPExchangerTest");
        System.out.print(" [-c #cities]");
        System.out.print(" [-p #poolSize]");
        System.out.print(" [-g #generations]");
        System.out.print(" [-n #pools]");
        System.out.print(" #threads]");
        System.out.println();
        System.exit(0);
    }

    /**
     * Perform one run with the given parameters.  Each run completes
     * when there are fewer than nThreads-2 tasks remaining.  This
     * avoids measuring termination effects, as well as cases where
     * the one last remaining task has no one left to exchange with,
     * so the pool is abruptly terminated.
     */
    static void oneRun(int nThreads, int nPools, int poolSize, int nGen) 
        throws InterruptedException {
        Population p = new Population(nThreads, nPools, poolSize, nGen);
        ProgressMonitor mon = null;
        if (verbose) {
            mon = new ProgressMonitor(p);
            mon.start();
        }
        p.printSnapshot(0);
        long startTime = System.nanoTime();
        p.start();
        p.awaitTasks();
        long stopTime = System.nanoTime();
        if (mon != null)
            mon.interrupt();
        p.shutdown();
        Thread.sleep(100);

        long elapsed = stopTime - startTime;
        long rate = elapsed / (nPools * nGen);
        double secs = (double)elapsed / 1000000000.0;
        p.printSnapshot(secs);
        System.out.printf("%10d ns per transfer\n", rate);
    }


    /**
     * A Population creates the pools, tasks, and threads for a run
     * and has control methods to start, stop, and report progress.
     */
    static final class Population {
        final Task[] tasks;
        final Exchanger<Strand> exchanger;
        final ThreadPoolExecutor exec;
        final CountDownLatch done;
        final int nGen;
        final int poolSize;
        final int nThreads;

        Population(int nThreads, int nPools, int poolSize, int nGen) {
            this.nThreads = nThreads;
            this.nGen = nGen;
            this.poolSize = poolSize;
            this.exchanger = new Exchanger<Strand>();
            this.done = new CountDownLatch(Math.max(1, nPools - nThreads - 2));
            this.tasks = new Task[nPools];
            for (int i = 0; i < nPools; i++) 
                tasks[i] = new Task(this);
            BlockingQueue<Runnable> tq = 
                new LinkedBlockingQueue<Runnable>();
            this.exec = new ThreadPoolExecutor(nThreads, nThreads,
                                               0L, TimeUnit.MILLISECONDS,
                                               tq);
            exec.prestartAllCoreThreads();
        }

        /** Start the tasks */
        void start() {
            for (int i = 0; i < tasks.length; i++) 
                exec.execute(tasks[i]);
        }

        /** Stop the tasks */
        void shutdown() {
            exec.shutdownNow();
        }

        /** Called by task upon terminations */
        void taskDone() {
            done.countDown();
        }

        /** Wait for (all but one) task to complete */
        void awaitTasks() throws InterruptedException {
            done.await();
        }

        /** 
         * Called by a task to resubmit itself after completing
         * fewer than nGen iterations.
         */
        void resubmit(Task task) {
            try {
                exec.execute(task);
            } catch(RejectedExecutionException ignore) {}
        }

        void printSnapshot(double secs) {
            int gens = 0;
            Chromosome bestc = tasks[0].chromosomes[0];
            Chromosome worstc = bestc;
            for (int k = 0; k < tasks.length; ++k) {
                gens += tasks[k].gen;
                Chromosome[] cs = tasks[k].chromosomes;
                if (cs[0].fitness < bestc.fitness)
                    bestc = cs[0];
                int w = cs[cs.length-1].fitness;
                if (cs[cs.length-1].fitness > worstc.fitness)
                    worstc = cs[cs.length-1];
            }
            double sqrtn = Math.sqrt(cities.length);
            double best = bestc.unitTourLength() / sqrtn;
            double worst = worstc.unitTourLength() / sqrtn;
            int avegen = (done.getCount() == 0)? nGen : gens / tasks.length;
            System.out.printf("Time:%9.3f Best:%7.3f Worst:%7.3f Gen:%6d Threads:%4d\n",
                              secs, best, worst, avegen, nThreads);
        }
        
    }

    /**
     * A Task updates its pool of chromosomes.. 
     */
    static final class Task implements Runnable {
        /** The pool of chromosomes, kept in sorted order */
        final Chromosome[] chromosomes;
        final Population pop;
        /** The common exchanger, same for all tasks */
        final Exchanger<Strand> exchanger;
        /** The current strand being exchanged */
        Strand strand;
        /** Bitset used in cross */
        final int[] inTour;
        final RNG rng;
        final int poolSize;
        final int nGen;
        final int genPerRun;
        int gen;

        Task(Population pop) {
            this.pop = pop;
            this.nGen = pop.nGen;
            this.gen = 0;
            this.poolSize = pop.poolSize;
            this.genPerRun = 4 * poolSize * Math.min(NCPUS, pop.nThreads);
            this.exchanger = pop.exchanger;
            this.rng = new RNG();
            int length = cities.length;
            this.strand = new Strand(length);
            this.inTour = new int[(length >>> 5) + 1];
            this.chromosomes = new Chromosome[poolSize];
            for (int j = 0; j < poolSize; ++j)
                chromosomes[j] = new Chromosome(length, rng);
            Arrays.sort(chromosomes);
        }

        /**
         * Run one or more update cycles.  An average of genPerRun
         * iterations are performed per run, and then the task is
         * resubmitted. The rate is proportional to both pool size and
         * number of threads.  This keeps average rate of breeding
         * across pools approximately constant across different test
         * runs.
         */
        public void run() {
            try {
                int maxGen = gen + 1 + rng.next() % genPerRun;
                if (maxGen > nGen) 
                    maxGen = nGen;
                while (gen++ < maxGen)
                    update();
                if (maxGen < nGen) 
                    pop.resubmit(this);
                else
                    pop.taskDone();
            } catch (InterruptedException ie) {
                pop.taskDone();
            }
        }

        /**
         * Choose a breeder, exchange strand with another pool, and
         * cross them to create new chromosome to replace a chosen
         * dyer.
         */
        void update() throws InterruptedException {
            int b = chooseBreeder();
            int d = chooseDyer(b);
            Chromosome breeder = chromosomes[b];
            Chromosome child = chromosomes[d];
            chooseStrand(breeder);
            strand = exchanger.exchange(strand);
            cross(breeder, child);
            fixOrder(child, d);
        }

        /**
         * Choose a breeder, with exponentially decreasing probability
         * starting at best.
         * @return index of selected breeder
         */
        int chooseBreeder() {
            int mask = (poolSize >>> BREEDER_DECAY) - 1;
            int b = 0;
            while ((rng.next() & mask) != mask) {
                if (++b >= poolSize)
                    b = 0;
            }
            return b;
        }

        /**
         * Choose a chromosome that will be replaced, with
         * exponentially decreasing probablility starting at
         * worst, ignoring the excluded index
         * @param exclude index to ignore; use -1 to not exclude any
         * @return index of selected dyer
         */
        int chooseDyer(int exclude) {
            int mask = (poolSize >>> DYER_DECAY)  - 1;
            int d = poolSize - 1;
            while (d == exclude || (rng.next() & mask) != mask) {
                if (--d < 0)
                    d = poolSize - 1;
            }
            return d;
        }

        /** 
         * Select a random strand of b's.
         * @param breeder the breeder
         */
        void chooseStrand(Chromosome breeder) {
            int[] bs = breeder.alleles;
            int length = bs.length;
            int strandLength = MIN_STRAND_LENGTH;
            while (strandLength < length &&
                   (rng.next() & RANDOM_STRAND_MASK) != RANDOM_STRAND_MASK)
                strandLength++;
            strand.strandLength = strandLength;
            int[] ss = strand.alleles;
            int k = (rng.next() & 0x7FFFFFFF) % length;
            for (int i = 0; i < strandLength; ++i) {
                ss[i] = bs[k];
                if (++k >= length) k = 0;
            }
        }

        /**
         * Copy current strand to start of c's, and then append all
         * remaining b's that aren't in the strand.
         * @param breeder the breeder
         * @param child the child
         */
        void cross(Chromosome breeder, Chromosome child) {
            for (int k = 0; k < inTour.length; ++k) // clear bitset
                inTour[k] = 0;

            // Copy current strand to c
            int[] cs = child.alleles;
            int ssize = strand.strandLength;
            int[] ss = strand.alleles;
            int i;
            for (i = 0; i < ssize; ++i) {
                int x = ss[i];
                cs[i] = x;
                inTour[x >>> 5] |= 1 << (x & 31); // record in bit set
            }

            // Find index of matching origin in b
            int first = cs[0];
            int j = 0;
            int[] bs = breeder.alleles;
            while (bs[j] != first) 
                ++j;

            // Append remaining b's that aren't already in tour
            while (i < cs.length) {
                if (++j >= bs.length) j = 0;
                int x = bs[j];
                if ((inTour[x >>> 5] & (1 << (x & 31))) == 0)
                    cs[i++] = x;
            } 

        }

        /**
         * Fix the sort order of a changed Chromosome c at position k
         * @param c the chromosome
         * @param k the index 
         */
        void fixOrder(Chromosome c, int k) {
            Chromosome[] cs = chromosomes;
            int oldFitness = c.fitness;
            c.recalcFitness();
            int newFitness = c.fitness;
            if (newFitness < oldFitness) {
                int j = k;
                int p = j - 1;
                while (p >= 0 && cs[p].fitness > newFitness) {
                    cs[j] = cs[p];
                    j = p--;
                }
                cs[j] = c;
            } else if (newFitness > oldFitness) {
                int j = k;
                int n = j + 1;
                while (n < cs.length && cs[n].fitness < newFitness) {
                    cs[j] = cs[n];
                    j = n++;
                }
                cs[j] = c;
            }
        }
    }

    /**
     * A Chromosome is a candidate TSP tour.
     */
    static final class Chromosome implements Comparable {
        /** Index of cities in tour order */
        final int[] alleles;
        /** Total tour length */
        int fitness;

        /** 
         * Initialize to random tour
         */
        Chromosome(int length, RNG random) {
            alleles = new int[length];
            for (int i = 0; i < length; i++)
                alleles[i] = i;
            for (int i = length - 1; i > 0; i--) {
                int idx = (random.next() & 0x7FFFFFFF) % alleles.length;
                int tmp = alleles[i];
                alleles[i] = alleles[idx];
                alleles[idx] = tmp;
            }
            recalcFitness();
        }

        public int compareTo(Object x) { // to enable sorting
            int xf = ((Chromosome)x).fitness;
            int f = fitness;
            return ((f == xf)? 0 :((f < xf)? -1 : 1));
        }

        void recalcFitness() {
            int[] a = alleles;
            int len = a.length;
            int p = a[0];
            long f = cities.distanceBetween(a[len-1], p);
            for (int i = 1; i < len; i++) {
                int n = a[i];
                f += cities.distanceBetween(p, n);
                p = n;
            }
            fitness = (int)(f / len);
        }

        double unitTourLength() {
            int[] a = alleles;
            int len = a.length;
            int p = a[0];
            double f = cities.unitDistanceBetween(a[len-1], p);
            for (int i = 1; i < len; i++) {
                int n = a[i];
                f += cities.unitDistanceBetween(p, n);
                p = n;
            }
            return f;
        }

        void validate() { // Ensure that this is a valid tour.
            int len = alleles.length;
            boolean[] used = new boolean[len];
            for (int i = 0; i < len; ++i) 
                used[alleles[i]] = true;
            for (int i = 0; i < len; ++i) 
                if (!used[i])
                    throw new Error("Bad tour");
        }

    }

    /**
     * A Strand is a random sub-sequence of a Chromosome.  Each task
     * creates only one strand, and then trades it with others,
     * refilling it on each iteration.
     */
    static final class Strand {
        final int[] alleles;
        int strandLength;
        Strand(int length) { alleles = new int[length]; }
    }

    /**
     * A collection of (x,y) points that represent cities. 
     */
    static final class CitySet {

        final int length;
        final int[] xPts;
        final int[] yPts;
        final int[][] distances;

        CitySet(int n) {
            this.length = n;
            this.xPts = new int[n];
            this.yPts = new int[n];
            this.distances = new int[n][n];

            RNG random = new RNG();
            for (int i = 0; i < n; i++) {
                xPts[i] = (random.next() & 0x7FFFFFFF);
                yPts[i] = (random.next() & 0x7FFFFFFF);
            }

            for (int i = 0; i < n; i++) {
                for (int j = 0; j < n; j++) {
                    double dx = (double)xPts[i] - (double)xPts[j];
                    double dy = (double)yPts[i] - (double)yPts[j];
                    double dd = Math.hypot(dx, dy) / 2.0;
                    long ld = Math.round(dd);
                    distances[i][j] = (ld >= Integer.MAX_VALUE)? 
                        Integer.MAX_VALUE : (int)ld;
                }
            }
        }

        /**
         *  Returns the cached distance between a pair of cities
         */
        int distanceBetween(int i, int j) {
            return distances[i][j];
        }

        // Scale ints to doubles in [0,1)
        static final double PSCALE = (double)0x80000000L;

        /**
         * Return distance for points scaled in [0,1). This simplifies
         * checking results.  The expected optimal TSP for random
         * points is believed to be around 0.76 * sqrt(N). For papers
         * discussing this, see
         * http://www.densis.fee.unicamp.br/~moscato/TSPBIB_home.html
         */
        double unitDistanceBetween(int i, int j) {
            double dx = ((double)xPts[i] - (double)xPts[j]) / PSCALE;
            double dy = ((double)yPts[i] - (double)yPts[j]) / PSCALE;
            return Math.hypot(dx, dy);
        }
            
    }

    /**
     * Cheap XorShift random number generator
     */
    static final class RNG {
        /** Seed generator for XorShift RNGs */
        static final Random seedGenerator = new Random();

        int seed;
        RNG(int seed) { this.seed = seed; }
        RNG()         { this.seed = seedGenerator.nextInt() | 1;  }

        int next() {
            int x = seed;
            x ^= x << 6;
            x ^= x >>> 21;
            x ^= x << 7;
            seed = x;
            return x;
        }
    }

    static final class ProgressMonitor extends Thread {
        final Population pop;
        ProgressMonitor(Population p) { pop = p; }
        public void run() {
            double time = 0;
            try {
                while (!Thread.interrupted()) {
                    sleep(SNAPSHOT_RATE);
                    time += SNAPSHOT_RATE;
                    pop.printSnapshot(time / 1000.0);
                }
            } catch (InterruptedException ie) {}
        }
    }
}
Revision:	1.3
Committed:	Mon Dec 12 20:06:20 2005 UTC (18 years, 5 months ago) by dl
Branch:	MAIN
Changes since 1.2:	+94 -78 lines
Log Message:	Improve as performance test
#	Content
1	/*
2	* Written by Doug Lea and Bill Scherer with assistance from members
3	* of JCP JSR-166 Expert Group and released to the public domain, as
4	* explained at http://creativecommons.org/licenses/publicdomain
5	*/
6
7	import java.util.*;
8	import java.util.concurrent.*;
9	import java.util.concurrent.atomic.*;
10	import java.util.concurrent.locks.*;
11
12	/**
13	* A parallel Traveling Salesperson Problem (TSP) program based on a
14	* genetic algorithm using an Exchanger. A population of chromosomes
15	* is distributed among "pools". The chromosomes represent tours, and
16	* their fitness is the total tour length. A Task is associated with
17	* each pool. Each task repeatedly does, for a fixed number of
18	* iterations (generations):
19	* <ol>
20	* <li> Select a breeder b from the pool
21	* <li> Create a strand of its tour with a random starting point and length
22	* <li> Offer the strand to the exchanger, receiving a strand from
23	* another pool
24	* <li> Combine b and the received strand using crossing function to
25	* create new chromosome c.
26	* <li> Replace a chromosome in the pool with c.
27	* </ol>
28	*
29	* See below for more details.
30	* <p>
31	*
32	*/
33	public class TSPExchangerTest {
34	static final int NCPUS = Runtime.getRuntime().availableProcessors();
35
36	static final int DEFAULT_MAX_THREADS = NCPUS + 6;
37
38	/**
39	* The problem size. Each city is a random point. The goal is to
40	* find a tour among them with smallest total Euclidean distance.
41	*/
42	static final int DEFAULT_CITIES = 144;
43
44	// Tuning parameters.
45
46	/**
47	* The number of chromosomes per pool. Must be a power of two.
48	*
49	* Smaller values lead to faster iterations but poorer quality
50	* results
51	*/
52	static final int DEFAULT_POOL_SIZE = 32;
53
54	/**
55	* The number of iterations per task. Convergence appears
56	* to be roughly proportional to #cities-squared
57	*/
58	static final int DEFAULT_GENERATIONS = DEFAULT_CITIES * DEFAULT_CITIES;
59
60	/**
61	* The number of pools. The total population is #pools * poolSize,
62	* which should be roughly on the order of #cities-squared
63	*
64	* Smaller values lead to faster total runs but poorer quality
65	* results
66	*/
67	static final int DEFAULT_NPOOLS = DEFAULT_GENERATIONS / DEFAULT_POOL_SIZE;
68
69	/**
70	* The minimum length for a random chromosome strand.
71	* Must be at least 1.
72	*/
73	static final int MIN_STRAND_LENGTH = 3;
74
75	/**
76	* The probablility mask value for creating random strands,
77	* that have lengths at least MIN_STRAND_LENGTH, and grow
78	* with exposnential decay 2^(-(1/(RANDOM_STRAND_MASK + 1)
79	* Must be 1 less than a power of two.
80	*/
81	static final int RANDOM_STRAND_MASK = 7;
82
83	/**
84	* Probablility control for selecting breeders.
85	* Breeders are selected starting at the best-fitness chromosome,
86	* with exponentially decaying probablility
87	* 1 / (poolSize >>> BREEDER_DECAY).
88	*
89	* Larger values usually cause faster convergence but poorer
90	* quality results
91	*/
92	static final int BREEDER_DECAY = 1;
93
94	/**
95	* Probablility control for selecting dyers.
96	* Dyers are selected starting at the worst-fitness chromosome,
97	* with exponentially decaying probablility
98	* 1 / (poolSize >>> DYER_DECAY)
99	*
100	* Larger values usually cause faster convergence but poorer
101	* quality results
102	*/
103	static final int DYER_DECAY = 1;
104
105	static final boolean verbose = false;
106	static final long SNAPSHOT_RATE = 10000; // in milliseconds
107
108	/**
109	* The set of cities. Created once per program run, to
110	* make it easier to compare solutions across different runs.
111	*/
112	static CitySet cities;
113
114	public static void main(String[] args) throws Exception {
115	int maxThreads = DEFAULT_MAX_THREADS;
116	int nCities = DEFAULT_CITIES;
117	int poolSize = DEFAULT_POOL_SIZE;
118	int nGen = nCities * nCities;
119	int nPools = nCities * nCities / poolSize;
120
121	try {
122	int argc = 0;
123	while (argc < args.length) {
124	String option = args[argc++];
125	if (option.equals("-c")) {
126	nCities = Integer.parseInt(args[argc]);
127	nGen = nCities * nCities;
128	nPools = nCities * nCities / poolSize;
129	}
130	else if (option.equals("-p"))
131	poolSize = Integer.parseInt(args[argc]);
132	else if (option.equals("-g"))
133	nGen = Integer.parseInt(args[argc]);
134	else if (option.equals("-n"))
135	nPools = Integer.parseInt(args[argc]);
136	else
137	maxThreads = Integer.parseInt(option);
138	argc++;
139	}
140	}
141	catch (Exception e) {
142	reportUsageErrorAndDie();
143	}
144
145	System.out.print("TSPExchangerTest");
146	System.out.print(" -c " + nCities);
147	System.out.print(" -g " + nGen);
148	System.out.print(" -p " + poolSize);
149	System.out.print(" -n " + nPools);
150	System.out.print(" max threads " + maxThreads);
151	System.out.println();
152
153	cities = new CitySet(nCities);
154
155	for (int i = 2; i <= maxThreads; i += 2)
156	oneRun(i, nPools, poolSize, nGen);
157	}
158
159	static void reportUsageErrorAndDie() {
160	System.out.print("usage: TSPExchangerTest");
161	System.out.print(" [-c #cities]");
162	System.out.print(" [-p #poolSize]");
163	System.out.print(" [-g #generations]");
164	System.out.print(" [-n #pools]");
165	System.out.print(" #threads]");
166	System.out.println();
167	System.exit(0);
168	}
169
170	/**
171	* Perform one run with the given parameters. Each run completes
172	* when there are fewer than nThreads-2 tasks remaining. This
173	* avoids measuring termination effects, as well as cases where
174	* the one last remaining task has no one left to exchange with,
175	* so the pool is abruptly terminated.
176	*/
177	static void oneRun(int nThreads, int nPools, int poolSize, int nGen)
178	throws InterruptedException {
179	Population p = new Population(nThreads, nPools, poolSize, nGen);
180	ProgressMonitor mon = null;
181	if (verbose) {
182	mon = new ProgressMonitor(p);
183	mon.start();
184	}
185	p.printSnapshot(0);
186	long startTime = System.nanoTime();
187	p.start();
188	p.awaitTasks();
189	long stopTime = System.nanoTime();
190	if (mon != null)
191	mon.interrupt();
192	p.shutdown();
193	Thread.sleep(100);
194
195	long elapsed = stopTime - startTime;
196	long rate = elapsed / (nPools * nGen);
197	double secs = (double)elapsed / 1000000000.0;
198	p.printSnapshot(secs);
199	System.out.printf("%10d ns per transfer\n", rate);
200	}
201
202
203	/**
204	* A Population creates the pools, tasks, and threads for a run
205	* and has control methods to start, stop, and report progress.
206	*/
207	static final class Population {
208	final Task[] tasks;
209	final Exchanger<Strand> exchanger;
210	final ThreadPoolExecutor exec;
211	final CountDownLatch done;
212	final int nGen;
213	final int poolSize;
214	final int nThreads;
215
216	Population(int nThreads, int nPools, int poolSize, int nGen) {
217	this.nThreads = nThreads;
218	this.nGen = nGen;
219	this.poolSize = poolSize;
220	this.exchanger = new Exchanger<Strand>();
221	this.done = new CountDownLatch(Math.max(1, nPools - nThreads - 2));
222	this.tasks = new Task[nPools];
223	for (int i = 0; i < nPools; i++)
224	tasks[i] = new Task(this);
225	BlockingQueue<Runnable> tq =
226	new LinkedBlockingQueue<Runnable>();
227	this.exec = new ThreadPoolExecutor(nThreads, nThreads,
228	0L, TimeUnit.MILLISECONDS,
229	tq);
230	exec.prestartAllCoreThreads();
231	}
232
233	/** Start the tasks */
234	void start() {
235	for (int i = 0; i < tasks.length; i++)
236	exec.execute(tasks[i]);
237	}
238
239	/** Stop the tasks */
240	void shutdown() {
241	exec.shutdownNow();
242	}
243
244	/** Called by task upon terminations */
245	void taskDone() {
246	done.countDown();
247	}
248
249	/** Wait for (all but one) task to complete */
250	void awaitTasks() throws InterruptedException {
251	done.await();
252	}
253
254	/**
255	* Called by a task to resubmit itself after completing
256	* fewer than nGen iterations.
257	*/
258	void resubmit(Task task) {
259	try {
260	exec.execute(task);
261	} catch(RejectedExecutionException ignore) {}
262	}
263
264	void printSnapshot(double secs) {
265	int gens = 0;
266	Chromosome bestc = tasks[0].chromosomes[0];
267	Chromosome worstc = bestc;
268	for (int k = 0; k < tasks.length; ++k) {
269	gens += tasks[k].gen;
270	Chromosome[] cs = tasks[k].chromosomes;
271	if (cs[0].fitness < bestc.fitness)
272	bestc = cs[0];
273	int w = cs[cs.length-1].fitness;
274	if (cs[cs.length-1].fitness > worstc.fitness)
275	worstc = cs[cs.length-1];
276	}
277	double sqrtn = Math.sqrt(cities.length);
278	double best = bestc.unitTourLength() / sqrtn;
279	double worst = worstc.unitTourLength() / sqrtn;
280	int avegen = (done.getCount() == 0)? nGen : gens / tasks.length;
281	System.out.printf("Time:%9.3f Best:%7.3f Worst:%7.3f Gen:%6d Threads:%4d\n",
282	secs, best, worst, avegen, nThreads);
283	}
284
285	}
286
287	/**
288	* A Task updates its pool of chromosomes..
289	*/
290	static final class Task implements Runnable {
291	/** The pool of chromosomes, kept in sorted order */
292	final Chromosome[] chromosomes;
293	final Population pop;
294	/** The common exchanger, same for all tasks */
295	final Exchanger<Strand> exchanger;
296	/** The current strand being exchanged */
297	Strand strand;
298	/** Bitset used in cross */
299	final int[] inTour;
300	final RNG rng;
301	final int poolSize;
302	final int nGen;
303	final int genPerRun;
304	int gen;
305
306	Task(Population pop) {
307	this.pop = pop;
308	this.nGen = pop.nGen;
309	this.gen = 0;
310	this.poolSize = pop.poolSize;
311	this.genPerRun = 4 * poolSize * Math.min(NCPUS, pop.nThreads);
312	this.exchanger = pop.exchanger;
313	this.rng = new RNG();
314	int length = cities.length;
315	this.strand = new Strand(length);
316	this.inTour = new int[(length >>> 5) + 1];
317	this.chromosomes = new Chromosome[poolSize];
318	for (int j = 0; j < poolSize; ++j)
319	chromosomes[j] = new Chromosome(length, rng);
320	Arrays.sort(chromosomes);
321	}
322
323	/**
324	* Run one or more update cycles. An average of genPerRun
325	* iterations are performed per run, and then the task is
326	* resubmitted. The rate is proportional to both pool size and
327	* number of threads. This keeps average rate of breeding
328	* across pools approximately constant across different test
329	* runs.
330	*/
331	public void run() {
332	try {
333	int maxGen = gen + 1 + rng.next() % genPerRun;
334	if (maxGen > nGen)
335	maxGen = nGen;
336	while (gen++ < maxGen)
337	update();
338	if (maxGen < nGen)
339	pop.resubmit(this);
340	else
341	pop.taskDone();
342	} catch (InterruptedException ie) {
343	pop.taskDone();
344	}
345	}
346
347	/**
348	* Choose a breeder, exchange strand with another pool, and
349	* cross them to create new chromosome to replace a chosen
350	* dyer.
351	*/
352	void update() throws InterruptedException {
353	int b = chooseBreeder();
354	int d = chooseDyer(b);
355	Chromosome breeder = chromosomes[b];
356	Chromosome child = chromosomes[d];
357	chooseStrand(breeder);
358	strand = exchanger.exchange(strand);
359	cross(breeder, child);
360	fixOrder(child, d);
361	}
362
363	/**
364	* Choose a breeder, with exponentially decreasing probability
365	* starting at best.
366	* @return index of selected breeder
367	*/
368	int chooseBreeder() {
369	int mask = (poolSize >>> BREEDER_DECAY) - 1;
370	int b = 0;
371	while ((rng.next() & mask) != mask) {
372	if (++b >= poolSize)
373	b = 0;
374	}
375	return b;
376	}
377
378	/**
379	* Choose a chromosome that will be replaced, with
380	* exponentially decreasing probablility starting at
381	* worst, ignoring the excluded index
382	* @param exclude index to ignore; use -1 to not exclude any
383	* @return index of selected dyer
384	*/
385	int chooseDyer(int exclude) {
386	int mask = (poolSize >>> DYER_DECAY) - 1;
387	int d = poolSize - 1;
388	while (d == exclude \|\| (rng.next() & mask) != mask) {
389	if (--d < 0)
390	d = poolSize - 1;
391	}
392	return d;
393	}
394
395	/**
396	* Select a random strand of b's.
397	* @param breeder the breeder
398	*/
399	void chooseStrand(Chromosome breeder) {
400	int[] bs = breeder.alleles;
401	int length = bs.length;
402	int strandLength = MIN_STRAND_LENGTH;
403	while (strandLength < length &&
404	(rng.next() & RANDOM_STRAND_MASK) != RANDOM_STRAND_MASK)
405	strandLength++;
406	strand.strandLength = strandLength;
407	int[] ss = strand.alleles;
408	int k = (rng.next() & 0x7FFFFFFF) % length;
409	for (int i = 0; i < strandLength; ++i) {
410	ss[i] = bs[k];
411	if (++k >= length) k = 0;
412	}
413	}
414
415	/**
416	* Copy current strand to start of c's, and then append all
417	* remaining b's that aren't in the strand.
418	* @param breeder the breeder
419	* @param child the child
420	*/
421	void cross(Chromosome breeder, Chromosome child) {
422	for (int k = 0; k < inTour.length; ++k) // clear bitset
423	inTour[k] = 0;
424
425	// Copy current strand to c
426	int[] cs = child.alleles;
427	int ssize = strand.strandLength;
428	int[] ss = strand.alleles;
429	int i;
430	for (i = 0; i < ssize; ++i) {
431	int x = ss[i];
432	cs[i] = x;
433	inTour[x >>> 5] \|= 1 << (x & 31); // record in bit set
434	}
435
436	// Find index of matching origin in b
437	int first = cs[0];
438	int j = 0;
439	int[] bs = breeder.alleles;
440	while (bs[j] != first)
441	++j;
442
443	// Append remaining b's that aren't already in tour
444	while (i < cs.length) {
445	if (++j >= bs.length) j = 0;
446	int x = bs[j];
447	if ((inTour[x >>> 5] & (1 << (x & 31))) == 0)
448	cs[i++] = x;
449	}
450
451	}
452
453	/**
454	* Fix the sort order of a changed Chromosome c at position k
455	* @param c the chromosome
456	* @param k the index
457	*/
458	void fixOrder(Chromosome c, int k) {
459	Chromosome[] cs = chromosomes;
460	int oldFitness = c.fitness;
461	c.recalcFitness();
462	int newFitness = c.fitness;
463	if (newFitness < oldFitness) {
464	int j = k;
465	int p = j - 1;
466	while (p >= 0 && cs[p].fitness > newFitness) {
467	cs[j] = cs[p];
468	j = p--;
469	}
470	cs[j] = c;
471	} else if (newFitness > oldFitness) {
472	int j = k;
473	int n = j + 1;
474	while (n < cs.length && cs[n].fitness < newFitness) {
475	cs[j] = cs[n];
476	j = n++;
477	}
478	cs[j] = c;
479	}
480	}
481	}
482
483	/**
484	* A Chromosome is a candidate TSP tour.
485	*/
486	static final class Chromosome implements Comparable {
487	/** Index of cities in tour order */
488	final int[] alleles;
489	/** Total tour length */
490	int fitness;
491
492	/**
493	* Initialize to random tour
494	*/
495	Chromosome(int length, RNG random) {
496	alleles = new int[length];
497	for (int i = 0; i < length; i++)
498	alleles[i] = i;
499	for (int i = length - 1; i > 0; i--) {
500	int idx = (random.next() & 0x7FFFFFFF) % alleles.length;
501	int tmp = alleles[i];
502	alleles[i] = alleles[idx];
503	alleles[idx] = tmp;
504	}
505	recalcFitness();
506	}
507
508	public int compareTo(Object x) { // to enable sorting
509	int xf = ((Chromosome)x).fitness;
510	int f = fitness;
511	return ((f == xf)? 0 :((f < xf)? -1 : 1));
512	}
513
514	void recalcFitness() {
515	int[] a = alleles;
516	int len = a.length;
517	int p = a[0];
518	long f = cities.distanceBetween(a[len-1], p);
519	for (int i = 1; i < len; i++) {
520	int n = a[i];
521	f += cities.distanceBetween(p, n);
522	p = n;
523	}
524	fitness = (int)(f / len);
525	}
526
527	double unitTourLength() {
528	int[] a = alleles;
529	int len = a.length;
530	int p = a[0];
531	double f = cities.unitDistanceBetween(a[len-1], p);
532	for (int i = 1; i < len; i++) {
533	int n = a[i];
534	f += cities.unitDistanceBetween(p, n);
535	p = n;
536	}
537	return f;
538	}
539
540	void validate() { // Ensure that this is a valid tour.
541	int len = alleles.length;
542	boolean[] used = new boolean[len];
543	for (int i = 0; i < len; ++i)
544	used[alleles[i]] = true;
545	for (int i = 0; i < len; ++i)
546	if (!used[i])
547	throw new Error("Bad tour");
548	}
549
550	}
551
552	/**
553	* A Strand is a random sub-sequence of a Chromosome. Each task
554	* creates only one strand, and then trades it with others,
555	* refilling it on each iteration.
556	*/
557	static final class Strand {
558	final int[] alleles;
559	int strandLength;
560	Strand(int length) { alleles = new int[length]; }
561	}
562
563	/**
564	* A collection of (x,y) points that represent cities.
565	*/
566	static final class CitySet {
567
568	final int length;
569	final int[] xPts;
570	final int[] yPts;
571	final int[][] distances;
572
573	CitySet(int n) {
574	this.length = n;
575	this.xPts = new int[n];
576	this.yPts = new int[n];
577	this.distances = new int[n][n];
578
579	RNG random = new RNG();
580	for (int i = 0; i < n; i++) {
581	xPts[i] = (random.next() & 0x7FFFFFFF);
582	yPts[i] = (random.next() & 0x7FFFFFFF);
583	}
584
585	for (int i = 0; i < n; i++) {
586	for (int j = 0; j < n; j++) {
587	double dx = (double)xPts[i] - (double)xPts[j];
588	double dy = (double)yPts[i] - (double)yPts[j];
589	double dd = Math.hypot(dx, dy) / 2.0;
590	long ld = Math.round(dd);
591	distances[i][j] = (ld >= Integer.MAX_VALUE)?
592	Integer.MAX_VALUE : (int)ld;
593	}
594	}
595	}
596
597	/**
598	* Returns the cached distance between a pair of cities
599	*/
600	int distanceBetween(int i, int j) {
601	return distances[i][j];
602	}
603
604	// Scale ints to doubles in [0,1)
605	static final double PSCALE = (double)0x80000000L;
606
607	/**
608	* Return distance for points scaled in [0,1). This simplifies
609	* checking results. The expected optimal TSP for random
610	* points is believed to be around 0.76 * sqrt(N). For papers
611	* discussing this, see
612	* http://www.densis.fee.unicamp.br/~moscato/TSPBIB_home.html
613	*/
614	double unitDistanceBetween(int i, int j) {
615	double dx = ((double)xPts[i] - (double)xPts[j]) / PSCALE;
616	double dy = ((double)yPts[i] - (double)yPts[j]) / PSCALE;
617	return Math.hypot(dx, dy);
618	}
619
620	}
621
622	/**
623	* Cheap XorShift random number generator
624	*/
625	static final class RNG {
626	/** Seed generator for XorShift RNGs */
627	static final Random seedGenerator = new Random();
628
629	int seed;
630	RNG(int seed) { this.seed = seed; }
631	RNG() { this.seed = seedGenerator.nextInt() \| 1; }
632
633	int next() {
634	int x = seed;
635	x ^= x << 6;
636	x ^= x >>> 21;
637	x ^= x << 7;
638	seed = x;
639	return x;
640	}
641	}
642
643	static final class ProgressMonitor extends Thread {
644	final Population pop;
645	ProgressMonitor(Population p) { pop = p; }
646	public void run() {
647	double time = 0;
648	try {
649	while (!Thread.interrupted()) {
650	sleep(SNAPSHOT_RATE);
651	time += SNAPSHOT_RATE;
652	pop.printSnapshot(time / 1000.0);
653	}
654	} catch (InterruptedException ie) {}
655	}
656	}
657	}