Parallelism with Fork/Join in Java 7

As the number of processor cores available on modern hardware increases, it's becoming ever more important for developers to develop in ways that take advantage of the new hardware. IBM Developerworks has posted a multi-part series on the Fork-Join concurrency library, which is shipping as part of the upcoming Java 7 release. InfoQ covered the initial fork/join proposal for Java 7 previously, with feedback from the original author, Doug Lea. The concept of fork/join with respect to Java was originally introduced by Doug Lea in his paper 'Fork/Join Parallelism in Java'. His util.concurrent package was the foundation of JSR-166, which was the java.util.concurrent library released in Java 5. Fork/Join is simply a revision of this JSR.

Part 1 of the series details the central concepts of the fork-join library, and the problem it attempts to solve:

Going forward, the hardware trend is clear; Moore’s Law will not be delivering higher clock rates, but instead delivering more cores per chip. It is easy to imagine how you can keep a dozen processors busy using a coarse-grained task boundary such as a user request, but this technique will not scale to thousands of processors — traffic may scale exponentially for short periods of time, but eventually the hardware trend wins out. As we enter the many-core era, we will need to find finer-grained parallelism or risk keeping processors idle even though there is plenty of work to do. As the dominant hardware platform shifts, so too must the software platform if we wish to keep up. To this end, Java 7 will include a framework for representing a certain class of finer-grained parallel algorithms: the fork-join framework.

Fork-join embodies the technique of divide-and-conquer; take a problem and recursively break it down into subproblems until the subproblems are small enough that they can be more effectively solved sequentially. The recursive step involves dividing a problem into two or more subproblems, queueing the subproblems for solution (the fork step), waiting for the results of the subproblems (the join step), and merging the results.

The article then shows an example of the merge-sort algorithm using fork/join.

The last component covered in this series is the ParallelArray class. ParallelArray is a fork/join-enabled data structure that provides a general-purpose API for performing searching, filtering, and transforming on data sets in a highly concurrent manner.

The team working on the BGGA Closures proposal for Java have adapted the fork-join framework to work with closures, and have a working implementation on their proposal site. This Developerworks article series shows two examples of using the ParallelArray class - one without the closures proposal, and one with:

Here is an example of searching for a max GPA in a group of students using the current Java 7 fork/join proposal:

ParallelArray students = new ParallelArray(fjPool, data);
double bestGpa = students.withFilter(isSenior)
                         .withMapping(selectGpa)
                         .max();

public class Student {
    String name;
    int graduationYear;
    double gpa;
}

static final Ops.Predicate isSenior = new Ops.Predicate() {
    public boolean op(Student s) {
        return s.graduationYear == Student.THIS_YEAR;
    }
};

static final Ops.ObjectToDouble selectGpa = new Ops.ObjectToDouble() {
    public double op(Student student) {
        return student.gpa;
    }
};

Here is the same example using the BGGA Closures proposal:

double bestGpa = students.withFilter({Student s => (s.graduationYear == THIS_YEAR) })
                         .withMapping({ Student s => s.gpa })
                         .max();

Currently, Java 7 is expected for an early 2009 release.