--- jsr166/src/test/loops/TSPExchangerTest.java 2005/12/12 20:06:20 1.3
+++ jsr166/src/test/loops/TSPExchangerTest.java 2006/02/13 12:39:23 1.4
@@ -11,29 +11,42 @@ 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):
+ * genetic algorithm using an Exchanger. A population of chromosomes is
+ * distributed among "subpops". Each chromosomes represents a tour,
+ * and its fitness is the total tour length.
+ *
+ * A set of worker threads perform updates on subpops. The basic
+ * update step is:
*
- * - Select a breeder b from the pool
+ *
- Select a breeder b from the subpop
*
- Create a strand of its tour with a random starting point and length
*
- Offer the strand to the exchanger, receiving a strand from
- * another pool
+ * another subpop
*
- Combine b and the received strand using crossing function to
* create new chromosome c.
- *
- Replace a chromosome in the pool with c.
+ *
- Replace a chromosome in the subpop with c.
*
*
+ * This continues for a given number of generations per subpop.
+ * Because there are normally more subpops than threads, each worker
+ * thread performs small (randomly sized) run of updates for one
+ * subpop and then selects another. A run continues until there is at
+ * most one remaining thread performing updates.
+ *
* See below for more details.
- *
- *
*/
public class TSPExchangerTest {
static final int NCPUS = Runtime.getRuntime().availableProcessors();
- static final int DEFAULT_MAX_THREADS = NCPUS + 6;
+ /** Runs start with two threads, increasing by two through max */
+ static final int DEFAULT_MAX_THREADS = Math.max(4, NCPUS + NCPUS/2);
+
+ /** The number of replication runs per thread value */
+ static final int DEFAULT_REPLICATIONS = 3;
+
+ /** If true, print statistics in SNAPSHOT_RATE intervals */
+ static boolean verbose = true;
+ static final long SNAPSHOT_RATE = 10000; // in milliseconds
/**
* The problem size. Each city is a random point. The goal is to
@@ -44,27 +57,27 @@ public class TSPExchangerTest {
// Tuning parameters.
/**
- * The number of chromosomes per pool. Must be a power of two.
+ * The number of chromosomes per subpop. Must be a power of two.
*
* Smaller values lead to faster iterations but poorer quality
* results
*/
- static final int DEFAULT_POOL_SIZE = 32;
+ static final int DEFAULT_SUBPOP_SIZE = 32;
/**
- * The number of iterations per task. Convergence appears
+ * The number of iterations per subpop. 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,
+ * The number of subpops. The total population is #subpops * subpopSize,
* 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;
+ static final int DEFAULT_NSUBPOPS = DEFAULT_GENERATIONS / DEFAULT_SUBPOP_SIZE;
/**
* The minimum length for a random chromosome strand.
@@ -84,7 +97,7 @@ public class TSPExchangerTest {
* Probablility control for selecting breeders.
* Breeders are selected starting at the best-fitness chromosome,
* with exponentially decaying probablility
- * 1 / (poolSize >>> BREEDER_DECAY).
+ * 1 / (subpopSize >>> BREEDER_DECAY).
*
* Larger values usually cause faster convergence but poorer
* quality results
@@ -95,16 +108,13 @@ public class TSPExchangerTest {
* Probablility control for selecting dyers.
* Dyers are selected starting at the worst-fitness chromosome,
* with exponentially decaying probablility
- * 1 / (poolSize >>> DYER_DECAY)
+ * 1 / (subpopSize >>> 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.
@@ -114,9 +124,10 @@ public class TSPExchangerTest {
public static void main(String[] args) throws Exception {
int maxThreads = DEFAULT_MAX_THREADS;
int nCities = DEFAULT_CITIES;
- int poolSize = DEFAULT_POOL_SIZE;
+ int subpopSize = DEFAULT_SUBPOP_SIZE;
int nGen = nCities * nCities;
- int nPools = nCities * nCities / poolSize;
+ int nSubpops = nCities * nCities / subpopSize;
+ int nReps = DEFAULT_REPLICATIONS;
try {
int argc = 0;
@@ -125,14 +136,20 @@ public class TSPExchangerTest {
if (option.equals("-c")) {
nCities = Integer.parseInt(args[argc]);
nGen = nCities * nCities;
- nPools = nCities * nCities / poolSize;
+ nSubpops = nCities * nCities / subpopSize;
}
else if (option.equals("-p"))
- poolSize = Integer.parseInt(args[argc]);
+ subpopSize = Integer.parseInt(args[argc]);
else if (option.equals("-g"))
nGen = Integer.parseInt(args[argc]);
else if (option.equals("-n"))
- nPools = Integer.parseInt(args[argc]);
+ nSubpops = Integer.parseInt(args[argc]);
+ else if (option.equals("-q")) {
+ verbose = false;
+ argc--;
+ }
+ else if (option.equals("-r"))
+ nReps = Integer.parseInt(args[argc]);
else
maxThreads = Integer.parseInt(option);
argc++;
@@ -145,129 +162,149 @@ public class TSPExchangerTest {
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(" -p " + subpopSize);
+ System.out.print(" -n " + nSubpops);
+ System.out.print(" -r " + nReps);
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);
+ if (false && NCPUS > 4) {
+ int h = NCPUS/2;
+ System.out.printf("Threads: %4d Warmup\n", h);
+ oneRun(h, nSubpops, subpopSize, nGen);
+ Thread.sleep(500);
+ }
+
+ int maxt = (maxThreads < nSubpops) ? maxThreads : nSubpops;
+ for (int j = 0; j < nReps; ++j) {
+ for (int i = 2; i <= maxt; i += 2) {
+ System.out.printf("Threads: %4d Replication: %2d\n", i, j);
+ oneRun(i, nSubpops, subpopSize, nGen);
+ Thread.sleep(500);
+ }
+ }
}
static void reportUsageErrorAndDie() {
System.out.print("usage: TSPExchangerTest");
System.out.print(" [-c #cities]");
- System.out.print(" [-p #poolSize]");
+ System.out.print(" [-p #subpopSize]");
System.out.print(" [-g #generations]");
- System.out.print(" [-n #pools]");
+ System.out.print(" [-n #subpops]");
+ System.out.print(" [-r #replications]");
+ System.out.print(" [-q ]");
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.
+ * Perform one run with the given parameters. Each run complete
+ * when there are fewer than 2 active threads. When there is
+ * only one remaining thread, it will have no one to exchange
+ * with, so it is terminated (via interrupt).
*/
- static void oneRun(int nThreads, int nPools, int poolSize, int nGen)
+ static void oneRun(int nThreads, int nSubpops, int subpopSize, int nGen)
throws InterruptedException {
- Population p = new Population(nThreads, nPools, poolSize, nGen);
+ Population p = new Population(nThreads, nSubpops, subpopSize, nGen);
ProgressMonitor mon = null;
if (verbose) {
+ p.printSnapshot(0);
mon = new ProgressMonitor(p);
mon.start();
}
- p.printSnapshot(0);
long startTime = System.nanoTime();
p.start();
- p.awaitTasks();
+ p.awaitDone();
long stopTime = System.nanoTime();
if (mon != null)
mon.interrupt();
p.shutdown();
- Thread.sleep(100);
+ // 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
+ * A Population creates the subpops, subpops, and threads for a run
* and has control methods to start, stop, and report progress.
*/
static final class Population {
- final Task[] tasks;
+ final Worker[] threads;
+ final Subpop[] subpops;
final Exchanger exchanger;
- final ThreadPoolExecutor exec;
final CountDownLatch done;
final int nGen;
- final int poolSize;
+ final int subpopSize;
final int nThreads;
- Population(int nThreads, int nPools, int poolSize, int nGen) {
+ Population(int nThreads, int nSubpops, int subpopSize, int nGen) {
this.nThreads = nThreads;
this.nGen = nGen;
- this.poolSize = poolSize;
+ this.subpopSize = subpopSize;
this.exchanger = new Exchanger();
- 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 tq =
- new LinkedBlockingQueue();
- this.exec = new ThreadPoolExecutor(nThreads, nThreads,
- 0L, TimeUnit.MILLISECONDS,
- tq);
- exec.prestartAllCoreThreads();
+ this.done = new CountDownLatch(nThreads - 1);
+
+ this.subpops = new Subpop[nSubpops];
+ for (int i = 0; i < nSubpops; i++)
+ subpops[i] = new Subpop(this);
+
+ this.threads = new Worker[nThreads];
+ int maxExchanges = nGen * nSubpops / nThreads;
+ for (int i = 0; i < nThreads; ++i) {
+ threads[i] = new Worker(this, maxExchanges);
+ }
+
}
- /** Start the tasks */
void start() {
- for (int i = 0; i < tasks.length; i++)
- exec.execute(tasks[i]);
+ for (int i = 0; i < nThreads; ++i) {
+ threads[i].start();
+ }
}
/** Stop the tasks */
void shutdown() {
- exec.shutdownNow();
+ for (int i = 0; i < threads.length; ++ i)
+ threads[i].interrupt();
}
- /** Called by task upon terminations */
- void taskDone() {
+ void threadDone() {
done.countDown();
}
- /** Wait for (all but one) task to complete */
- void awaitTasks() throws InterruptedException {
+ /** Wait for tasks to complete */
+ void awaitDone() 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) {}
+ int totalExchanges() {
+ int xs = 0;
+ for (int i = 0; i < threads.length; ++i)
+ xs += threads[i].exchanges;
+ return xs;
}
+ /**
+ * Prints statistics, including best and worst tour lengths
+ * for points scaled in [0,1), scaled by the square root of
+ * number of points. 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
+ */
void printSnapshot(double secs) {
- int gens = 0;
- Chromosome bestc = tasks[0].chromosomes[0];
+ int xs = totalExchanges();
+ long rate = (xs == 0)? 0L : (long)((secs * 1000000000.0) / xs);
+ Chromosome bestc = subpops[0].chromosomes[0];
Chromosome worstc = bestc;
- for (int k = 0; k < tasks.length; ++k) {
- gens += tasks[k].gen;
- Chromosome[] cs = tasks[k].chromosomes;
+ for (int k = 0; k < subpops.length; ++k) {
+ Chromosome[] cs = subpops[k].chromosomes;
if (cs[0].fitness < bestc.fitness)
bestc = cs[0];
int w = cs[cs.length-1].fitness;
@@ -277,75 +314,109 @@ public class TSPExchangerTest {
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);
+ System.out.printf("N:%4d T:%8.3f B:%6.3f W:%6.3f X:%9d R:%7d\n",
+ nThreads, secs, best, worst, xs, rate);
+ // exchanger.printStats();
+ // System.out.print(" s: " + exchanger.aveSpins());
+ // System.out.print(" p: " + exchanger.aveParks());
}
-
}
/**
- * A Task updates its pool of chromosomes..
+ * Worker threads perform updates on subpops.
*/
- static final class Task implements Runnable {
- /** The pool of chromosomes, kept in sorted order */
+ static final class Worker extends Thread {
+ final Population pop;
+ final int maxExchanges;
+ int exchanges;
+ final RNG rng = new RNG();
+
+ Worker(Population pop, int maxExchanges) {
+ this.pop = pop;
+ this.maxExchanges = maxExchanges;
+ }
+
+ /**
+ * Repeatedly, find a subpop that is not being updated by
+ * another thread, and run a random number of updates on it.
+ */
+ public void run() {
+ try {
+ int len = pop.subpops.length;
+ int pos = (rng.next() & 0x7FFFFFFF) % len;
+ while (exchanges < maxExchanges) {
+ Subpop s = pop.subpops[pos];
+ AtomicBoolean busy = s.busy;
+ if (!busy.get() && busy.compareAndSet(false, true)) {
+ exchanges += s.runUpdates();
+ busy.set(false);
+ pos = (rng.next() & 0x7FFFFFFF) % len;
+ }
+ else if (++pos >= len)
+ pos = 0;
+ }
+ pop.threadDone();
+ } catch (InterruptedException fallthrough) {
+ }
+ }
+ }
+
+ /**
+ * A Subpop maintains a set of chromosomes..
+ */
+ static final class Subpop {
+ /** The chromosomes, kept in sorted order */
final Chromosome[] chromosomes;
+ /** The parent population */
final Population pop;
- /** The common exchanger, same for all tasks */
+ /** Reservation bit for worker threads */
+ final AtomicBoolean busy;
+ /** The common exchanger, same for all subpops */
final Exchanger 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;
+ final int subpopSize;
- Task(Population pop) {
+ Subpop(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.subpopSize = pop.subpopSize;
this.exchanger = pop.exchanger;
+ this.busy = new AtomicBoolean(false);
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)
+ this.chromosomes = new Chromosome[subpopSize];
+ for (int j = 0; j < subpopSize; ++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.
+ * Run a random number of updates. The number of updates is
+ * at least 1 and no more than subpopSize. This
+ * controls the granularity of multiplexing subpop updates on
+ * to threads. It is small enough to balance out updates
+ * across tasks, but large enough to avoid having runs
+ * dominated by subpop selection. It is randomized to avoid
+ * long runs where pairs of subpops exchange only with each
+ * other. It is hardwired because small variations of it
+ * don't matter much.
+ *
+ * @param g the first generation to run.
*/
- 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();
- }
+ int runUpdates() throws InterruptedException {
+ int n = 1 + (rng.next() & ((subpopSize << 1) - 1));
+ for (int i = 0; i < n; ++i)
+ update();
+ return n;
}
/**
- * Choose a breeder, exchange strand with another pool, and
+ * Choose a breeder, exchange strand with another subpop, and
* cross them to create new chromosome to replace a chosen
* dyer.
*/
@@ -366,10 +437,10 @@ public class TSPExchangerTest {
* @return index of selected breeder
*/
int chooseBreeder() {
- int mask = (poolSize >>> BREEDER_DECAY) - 1;
+ int mask = (subpopSize >>> BREEDER_DECAY) - 1;
int b = 0;
while ((rng.next() & mask) != mask) {
- if (++b >= poolSize)
+ if (++b >= subpopSize)
b = 0;
}
return b;
@@ -383,11 +454,11 @@ public class TSPExchangerTest {
* @return index of selected dyer
*/
int chooseDyer(int exclude) {
- int mask = (poolSize >>> DYER_DECAY) - 1;
- int d = poolSize - 1;
+ int mask = (subpopSize >>> DYER_DECAY) - 1;
+ int d = subpopSize - 1;
while (d == exclude || (rng.next() & mask) != mask) {
if (--d < 0)
- d = poolSize - 1;
+ d = subpopSize - 1;
}
return d;
}
@@ -524,6 +595,9 @@ public class TSPExchangerTest {
fitness = (int)(f / len);
}
+ /**
+ * Return tour length for points scaled in [0, 1).
+ */
double unitTourLength() {
int[] a = alleles;
int len = a.length;
@@ -537,7 +611,10 @@ public class TSPExchangerTest {
return f;
}
- void validate() { // Ensure that this is a valid tour.
+ /**
+ * Check that this tour visits each city
+ */
+ void validate() {
int len = alleles.length;
boolean[] used = new boolean[len];
for (int i = 0; i < len; ++i)
@@ -550,7 +627,7 @@ public class TSPExchangerTest {
}
/**
- * A Strand is a random sub-sequence of a Chromosome. Each task
+ * A Strand is a random sub-sequence of a Chromosome. Each subpop
* creates only one strand, and then trades it with others,
* refilling it on each iteration.
*/