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A number of people worked on replicating the results and optimizing the programs I listed in my earlier blog entry.

George Zervas profiled the program, found it was spending a lot of time garbage collecting, and used the -XX:+AggressiveHeap java runtime switch, which, according to its documentation

This option instructs the JVM to push memory use to the limit: the overall heap is more than 3850MB, the allocation area of each thread is 256K, the memory management policy defers collection as long as possible, and (beginning with J2SE 1.3.1_02) some GC activity is done in parallel.

The following diagram illustrates the results he obtained:

Vassileios Karakoidas, tried a different approach, storing the numbers into an ArrayList, moving them into a native array, and sorting them in place. This brings down the slowdown to 25%.

public class SortInt {
        public static void main(String args[]) {
                ArrayList<RInteger> s = new ArrayList<RInteger>();
                int i;
                int rand = 10;

                for (i = 0; i < 1000000; i++) {
                        s.add(new RInteger(rand));
                        rand = rand * 1103515245 + 12345;
                }

		RInteger[] rint = (RInteger[])s.toArray(new RInteger[0]);
		Arrays.sort(rint);
		int length = rint.length;

                for (i = 0;i < length; i++)
                        if (i % 100000 == 0)
                                System.out.println(rint[i]);
        }
}

Another optimization I tried involves using native integers or the Integer class in Java. Both appear to be more efficient, but, again, the program differs in the abstraction mechanisms used over its C++ sibling.

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