New JVM options and Scala iteration performance Monday, Oct 26 2009 

Internal iterators like foreach tend to do very well in micro-benchmarks on the JVM. In fact, they often do as well as the equivalent manual while or for loop. There is a catch, however, and it’s easy to miss it in micro-benchmarks.

A few months ago, David MacIver forwarded an email from Martin Odersky to the scala-internals mailing list about this. The problem as described by Martin:

“The reason seems to be that the foreach itself is not inlined, so the call to the closure becomes megamorphic and therefore slow.”

I was curious about the benchmarks used to measure this and Tiark Rompf (who had performed the measurements) provided the source code. I said I’d try to take a look the next weekend and I did it… more than 3 months later. Well, better late than never. ;)

There are 3 benchmarks:

  • Benchmark A: traverse a collection with N elements
  • Benchmark B: traverse a collection with N elements, inside the loop/closure traverse another collection with N2 elements 3 times
  • Benchmark C: build a collection (front to back) of N elements

Various approaches are used for each benchmark with various collections types. One weakness of the these benchmarks is that they don’t include a verification mechanism to ensure that all benchmarks produce the same result. However, they include code that tries to prevent the JIT from performing unfair optimisations (e.g. print something if an element in the collection matches a certain condition).

The original results produced by Tiark can be found here.

I added a few benchmarks that use plain arrays (RawArrayIndexed, RawArrayForeach, RawArrayForeachMega), made minor changes to the scripts and pushed the code to a GitHub repository. I left the rest of the Scala code as it was to make it easy to compare with the original results and ran the benchmark with various JVM settings to see what effect they would have. All the tests shared the following:

  • Dual quad-core Xeon E5462 2.80GHz
  • 14 GB RAM
  • Fedora 11
  • Scala 2.8.0.r19261
  • Revision 59521431f5c118b73e35b0b396e3efd6aecec3dd of project
  • 64-bit JDK 6 Update 18 early access b03
  • JVM base settings: -Xms1G -Xmx1G -XX:+UseParallelGC -XX:+UseParallelOldGC

JDK 6 Update 18 is scheduled to be released on Q4, 2009 and it includes HotSpot 16. Even though JDK 6 Update 14 (HotSpot 14) introduced compressed references and scalar replacement, HotSpot 16 includes improved compressed references and many crucial fixes to both features. According to my testing these features are now approaching production-level stability and the OpenJDK engineers seem to agree as they are both enabled by default in HotSpot 17 (which will eventually hit JDK6 too).

Interested in how these features would affect the performance in these benchmarks, I ran them with various combinations. I also added Scala’s compiler -optimise flag in some cases.

The original benchmark from Tiark used 3 collection types: array (java.util.ArrayList), list (scala.List, immutable single linked list) and vector (earlier version of immutable vector that has recently been added to Scala 2.8). I added JVM arrays and they are shown as “rawarray” in the charts. Finally, we get to the actual numbers.

Benchmark A

Click on chart for expanded version

There are some interesting data points here:

  1. Compressed references is a _huge_ win. RawArrayIndexed went from 500ms to 142ms and many of the vector operations were much faster.
  2. Escape analysis (which enables scalar replacement) doesn’t seem to have much of an effect.
  3. scalac -optimise doesn’t seem to have much of an effect.
  4. foreach is misleadingly fast in micro-benchmarks, but it’s easy to bring it down to earth. RawArrayForeach performs similarly to RawArrayIndexed, but RawArrayForeachMega is 10 times slower. The latter simply calls foreach with a few different anonymous functions during the collection creation phase causing the call site to become megamorphic. Once this happens, the only hope for good performance is that the foreach method gets inlined and it doesn’t seem to happen here. With this in mind, it seems like ticket 1338 (Optimize simple for loops) is a good idea.
Benchmark B

Click on chart for expanded version

Once again, compressed references are a large factor in some benchmarks (halving the time taken in some cases).

The new bit of information is that scalac -optimise causes a huge improvement in VectorForeachFast and VectorForeachFastProtect. This makes sense once one considers one of the findings from the previous benchmark. We said that inlining of foreach is of extreme importance once a call site is megamorphic and this is precisely what -optimise does in this case (and the JVM fails to do so at runtime otherwise). Sadly, -optimise cannot do this safely in many cases as it’s shown by the results for VectorForeach.

Benchmark C

Click on chart for expanded version

Once again, compressed references provide a nice boost. Seems like this option is a winner in 64-bit JVMs (if you don’t need a heap larger than 32GB), it saves memory and gives better performance. The usual disclaimer applies though, you should benchmark your own application instead of relying on micro-benchmarks when deciding what JVM options to use.

The complete results are also available. Feel free to play with the source code and provide your own numbers, fixes and/or improvements.

Objects with no allocation overhead Wednesday, Dec 17 2008 

We have all heard about how HotSpot is really good at dealing with short-lived objects (both allocation and GC), but the truth is that object allocation is still pretty costly when compared to operations like addition or multiplication. Allocating an object for each step of an iteration over a large collection to make a simple computation might sound like the kind of thing no-one would ever do, but it’s actually quite common in languages like Scala (as described in a previous post). In Java-land, if you use the Function class from Google Collections with primitive wrappers, the same issue may occur. There are many JVM improvements that could help depending on the specific case (generic specialisation, value types, fixnums to name a few), but it’s unclear if/when we’ll get them.

So, what about that title? Escape analysis was introduced during Java 6, but the information gathered was only used for lock elision. However, this information can also be used for other interesting optimisations like scalar replacement and stack allocation. There have been doubts about the benefits of stack allocation (discussed in the comments) so the focus has been on scalar replacement so that the object is never in memory. At least that’s the theory.

Edward Lee started a couple of threads in the Hotspot-dev mailing list about scalar replacement here and here which reminded me to do some experiments. Note that this feature is still in development so the results posted here are preliminary and not indicative of how it will perform once it’s final. Still, it’s interesting to see how well it works at this time. I picked the latest JDK7 build (build 41) and ran a few tests with the following arguments passed to java “-XX:MaxPermSize=256m -Xms128m -Xmx3328m -server -XX:+UseConcMarkSweepGC” and either XX:-DoEscapeAnalysis or XX:+DoEscapeAnalysis.

I started by choosing the simplest test possible. Note that either testSimpleAllocation or testNoAllocation would be commented out.

class C(val a: Int, val b: Int)

object Test {
  def main(args: Array[String]) {
    for (i <- 1 to 10) testSimpleAllocation()
    //for (i <- 1 to 10) testNoAllocation()
  }
  
  def testSimpleAllocation() = {
    System.gc()
    var time = System.currentTimeMillis;
    var i = 0
    var sum = 0
    while (i < 1000000000) {
      sum += baz(new C(i + 1, i + 2))
      i += 1
    }
    println(sum)
    println(System.currentTimeMillis - time)
  }
  
  def testNoAllocation() = {
    System.gc()
    var time = System.currentTimeMillis;
    var i = 0
    var sum = 0
    while (i < 1000000000) {
      sum += baz(i + 1, i + 2)
      i += 1
    }
    println(sum)
    println(System.currentTimeMillis - time)
  }
  
  def baz(a: Int, b: Int): Int = a + b
  
  def baz(c: C): Int = c.a + c.b
}

The result were:


testNoAllocation: 403
testSimpleAllocation with EA: 1006
testSimpleAllocation without EA: 9190

As we can see, escape analysis has a tremendous effect and the method finishes in 11% of the time taken with it disabled. However, the version with no allocation is still substantially faster.

I decided to test a foreach method that takes a Function object next (this time in Java because it does less magic behind the scenes):

package test;

public class EscapeAnalysis {
  
  interface Function<T, R> {
    R apply(T value);
  }
  
  interface IntProcedure {
    void apply(int value);
  }
  
  static class BoxedArray {
    private final int[] array;
    
    public BoxedArray(int length) {
      array = new int[length];
    }
    
    public int length() {
      return array.length;
    }
    
    public void foreach(Function<Integer, Void> function) {
      for (int i : array)
        function.apply(new Integer(i));
    }
    
    public void foreach(IntFunction function) {
      for (int i : array)
        function.apply(i);
    }

    public void set(int index, int value) {
      array[index] = value;
    }

    public void foreachWithAutoboxing(Function<Integer, Void> function) {
      for (int i : array)
        function.apply(i);
    }
  }
  
  public static void main(String[] args) {
    BoxedArray array = new BoxedArray(100000000);
    /* We are careful not to restrict our ints to the ones in the Integer.valueOf cache */
    for (int i = 0; i < array.length(); i++)
      array.set(i, i);
    
    for (int i = 0; i < 10; i++)
      test(array);
  }

  private static void test(BoxedArray array) {
    System.gc();
    long time = System.currentTimeMillis();
    final int[] sum = new int[] { 0 };
    
    /* Uncomment the one that should be executed */
    testIntegerForeach(array, sum);
//    testIntegerForeachWithAutoboxing(array, sum);
//    testIntForeach(array, sum);

    System.out.println(System.currentTimeMillis() - time);
    System.out.println(sum[0]);
  }
  
  private static void testIntegerForeachWithAutoboxing(BoxedArray array, final int[] sum) {
    array.foreachWithAutoboxing(new Function<Integer, Void>() {
      public Void apply(Integer value) {
        sum[0] += value;
        return null;
      }
    });
  }
  
  private static void testIntegerForeach(BoxedArray array, final int[] sum) {
    array.foreach(new Function<Integer, Void>() {
      public Void apply(Integer value) {
        sum[0] += value;
        return null;
      }
    });
  }

  private static void testIntForeach(BoxedArray array, final int[] sum) {
    array.foreach(new IntFunction() {
      public void apply(int value) {
        sum[0] += value;
      }
    });
  }
}

The results were:


testIntForeach: 130
testIntegerForeachWithAutoboxing with EA: 1064
testIntegerForeach with EA: 224
testIntegerForeachWithAutoboxing without EA: 1039
testIntegerForeach without EA: 1024

This test shows something interesting, EA gives no improvement if Integer.valueOf (called by auto-boxing) is used instead of new Integer. Apart from that, the results are somewhat similar to the first ones (EA provides a substantial boost, but not enough to match the specialised implementation). After quickly testing that the boxing methods in ScalaRunTime had the same effect as Integer.valueOf, I decided that it was not worth testing more complex scenarios.

It seems like there’s a lot of potential for scalar replacement, but HotSpot needs to do a better job at detecting cases where it can be used safely. If nothing else, at least knowledge of the valueOf methods should be hardcoded into the system. I hope that a more general solution is found though because other languages on the JVM may use different methods (as mentioned earlier Scala uses methods in ScalaRunTime instead). It will also be interesting to see if the performance can get even closer to the no allocation case. Since the feature is still in development, we can hope. :)

Update: The early access release of JDK 6 Update 14 also contains this feature.
Update 2: This feature is enabled by default since JDK 6 Update 23.