Java Tip: When to use ForkJoinPool vs ExecutorService

Know your options for multithreaded programming on the Java platform

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Listing 5. LinkFinderAction


import java.util.ArrayList;
import java.util.List;
import java.util.concurrent.RecursiveAction;
import org.htmlparser.Parser;
import org.htmlparser.filters.NodeClassFilter;
import org.htmlparser.tags.LinkTag;
import org.htmlparser.util.NodeList;
import insidecoding.webcrawler7.LinkHandler;

 * @author Madalin Ilie
public class LinkFinderAction extends RecursiveAction {

    private String url;
    private LinkHandler cr;
     * Used for statistics
    private static final long t0 = System.nanoTime();

    public LinkFinderAction(String url, LinkHandler cr) {
        this.url = url; = cr;

    public void compute() {
        if (!cr.visited(url)) {
            try {
                List<RecursiveAction> actions = new ArrayList<RecursiveAction>();
                URL uriLink = new URL(url);
                Parser parser = new Parser(uriLink.openConnection());
                NodeList list = parser.extractAllNodesThatMatch(new NodeClassFilter(LinkTag.class));

                for (int i = 0; i < list.size(); i++) {
                    LinkTag extracted = (LinkTag) list.elementAt(i);

                    if (!extracted.extractLink().isEmpty()
                            && !cr.visited(extracted.extractLink())) {

                        actions.add(new LinkFinderAction(extracted.extractLink(), cr));

                if (cr.size() == 1500) {
                    System.out.println("Time for visit 1500 distinct links= " + (System.nanoTime() - t0));                   

                //invoke recursively
            } catch (Exception e) {
                //ignore 404, unknown protocol or other server errors

The application logic so far is the same as it was in the Java 6 implementation. The difference in the code is that instead of manually queuing the new links through the LinkHandler class, we submit them to the ForkJoinPool through the invokeAll() static method. Note the invokeAll(actions) line. The ForkJoinPool will schedule these tasks in the best possible way using the available 64 threads. A recursive action is over when the submitted link has been visited (see if (!cr.visited(url))).

Comparative benchmarks for search coverage: 1,500 distinct links

Now it's time to compare benchmarks. I accounted for JVM warmup when timing the two different implementations: first I ran each program 10 times ignoring the results, then I ran it again 10 times again to compute an average timing. Between running the Java 6 and Java 7 code I also called System.gc() numerous times to manually activate the garbage collector. I invoked both applications using the JVM flags -d64 -Xmx1512m, thus setting the platform to 64 bits and the maximum heap size to 1512 MB (see Resources).

I ran the tests on a Windows 7 SP1 64-bit machine, Intel Core i5 @2.67 GHz with 4,00 GB of RAM. I have installed the 64-bit version of JDK 7 update 2.

The timing of the Java 6 code is as follows (an average of all 10 runs):

Time to visit 1,500 distinct links: 45,404,628,454 nanoseconds
Fastest time: 43,989,514,242 nanoseconds
Slowest time: 47,077,714,098 nanoseconds

And here's the timing for the Java 7 implementation:

Time to visit 1,500 distinct links: 45,269,306,013 nanoseconds
Fastest time: 42,365,714,625 nanoseconds
Slowest time: 59,042,391,887 nanoseconds

As you can see, when accounting for search coverage (tasked with following 1,500 distinct links) there's not much difference between the two implementations.

Comparative benchmarks for processing power: 3,000 non-distinct links

In order to to test the second scenario I had to make some adjustments to both implementations. In both the WebCrawler6 and WebCrawler7 classes, I uncommented the synchronized List and commented the synchronized Set. For a benchmark based on following non-distinct links the Set implementation isn't required, but the List is.

// private final Collection<String> visitedLinks = Collections.synchronizedSet(new HashSet<String>());

private final Collection<String> visitedLinks = Collections.synchronizedList(new ArrayList<String>());

I also changed the visited() method to always return false, because for this benchmark it doesn't matter whether a link has been visited or not.

    public boolean visited(String s) {
        return false;//visitedLinks.contains(s);

Finally, I changed the conditions in the LinkFinder and LinkFinderAction classes to check for 3,000 links instead of 1,500:

if (cr.size() == 3000) {
System.out.println("Time for visit 3000 non-distinct links= " + (System.nanoTime() - t0));

The resulting benchmarks show that Fork/Join fared better when measuring processing power -- i.e., how many links each application processed per second.

Here's the timing of the Java 6 code, an average of the results for all 10 runs:

Time to visit 3,000 non-distinct links: 48,510,285,967 nanoseconds
Fastest time: 44,189,380,355 nanoseconds 
Slowest time: 52,132,053,413 nanoseconds

This measurement is equivalent to 61.8425 links per second.

And here's the timing for the program written using Java 7:

Time to visit 3,000 non-distinct links: 31,343,446,584 nanoseconds
Fastest time: 30,533,600,312 nanoseconds 
Slowest time: 33,308,851,937 nanoseconds

This is equivalent to 95.7137 links per second.

The code based on Java 7's ForkJoinPool was 1.5x times faster than the Java 6 code -- a significant performance gain.

Figures 1 and 2 shows the CPU history for each implementation. Note that CPU usage is pretty much the same, even though the ForkJoinPool implementation is faster.

Figure 1. CPU usage for the Java 6 ExecutorService implementation

Figure 2. CPU usage for the Java 7 ForkJoinPool implementation

In conclusion: Fork/Join for recursive programming

While relatively simple, my benchmarks demonstrate that Fork/Join offers serious gains for solving problems that involve recursion. Because recursion is fundamental to parallel programming on multicore platforms (see Resources) Fork/Join is an essential addition to Java platform concurrency. That said, it does not replace the original java.util.concurrency package. As I've demonstrated, ExecutorService continues to be a fine solution for many concurrent programming tasks. In a programming scenario such as the one I set up, where effective recursion is key to processing power, Fork/Join is likely to be the most effective solution.

Learn more

This article has briefly introduced two approaches to Java concurrency and demonstrated each one's applicability to two common program requirements: data collection and search coverage. See the Resources section to learn more about java.util.concurrency and the uses of Fork/Join in Java 7.

Madalin Ilie is a software development lead at Endava Romania. He has more than five years' programming experience and has worked in a variety of project domains, from mobile development to heavy financial applications.

This story, "Java Tip: When to use ForkJoinPool vs ExecutorService" was originally published by JavaWorld.

Copyright © 2011 IDG Communications, Inc.

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