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POjava immutable class much slower
 

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    <p>I was in need of some Complex math library, so I hesitated between libraries that use immutable Complex and libraries that use mutable Complex. Obviously, I want computations to run reasonably fast (unless it kills readability etc.).</p> <p>So I created simple test of speed mutable vs immutable:</p> <pre><code>final class MutableInt { private int value; public int getValue() { return value; } public void setValue(int value) { this.value = value; } public MutableInt() { this(0); } public MutableInt(int value) { this.value = value; } } final class ImmutableInt { private final int value; public ImmutableInt(int value) { this.value = value; } public int getValue() { return value; } } public class TestImmutableSpeed { static long testMutable(final int arrLen) { MutableInt[] arrMutable = new MutableInt[arrLen]; for (int i = 0; i &lt; arrMutable.length; ++i) { arrMutable[i] = new MutableInt(i); for (int j = 0; j &lt; arrMutable.length; ++j) { arrMutable[i].setValue(arrMutable[i].getValue() + j); } } long sumMutable = 0; for (MutableInt item : arrMutable) { sumMutable += item.getValue(); } return sumMutable; } static long testImmutable(final int arrLen) { ImmutableInt[] arrImmutable = new ImmutableInt[arrLen]; for (int i = 0; i &lt; arrImmutable.length; ++i) { arrImmutable[i] = new ImmutableInt(i); for (int j = 0; j &lt; arrImmutable.length; ++j) { arrImmutable[i] = new ImmutableInt(arrImmutable[i].getValue() + j); } } long sumImmutable = 0; for (ImmutableInt item : arrImmutable) { sumImmutable += item.getValue(); } return sumImmutable; } public static void main(String[] args) { final int arrLen = 1&lt;&lt;14; long tmStart = System.nanoTime(); System.out.println("sum = " + testMutable(arrLen)); long tmMid = System.nanoTime(); System.out.println("sum = " + testImmutable(arrLen)); long tmEnd = System.nanoTime(); System.out.println("speed comparison mutable vs immutable:"); System.out.println("mutable " + (tmMid - tmStart)/1000000 + " ms"); System.out.println("immutable " + (tmEnd - tmMid)/1000000 + " ms"); } } </code></pre> <p>You can adjust size of array if the test runs too slow/fast.</p> <p>I run with: -server -Xms256m -XX:+AggressiveOpts And I get:</p> <pre>sum = 2199023247360 sum = 2199023247360 speed comparison mutable vs immutable: <b>mutable 102 ms immutable 1506 ms</b></pre> <p>Question: Am I missing some optimization parameter, or is immutable version 15x slower?</p> <p>If it is, why would anyone write math library with immutable class Complex in it? Is immutable just "fancy" but useless? </p> <p>I know that immutable class is safer as hash map key or can't have race conditions, but that are special cases that can be handled without immutability everywhere.</p> <p><strong>Edit:</strong> I re-run this microbenchmark with caliper, as suggested by one answer, and it runs 12x slower, not 15x, still the same point. Changed code for Caliper benchmarking:</p> <pre> import com.google.caliper.Runner; import com.google.caliper.SimpleBenchmark; final class MutableInt { private int value; public int getValue() { return value; } public void setValue(int value) { this.value = value; } public MutableInt() { this(0); } public MutableInt(int value) { this.value = value; } } final class ImmutableInt { private final int value; public ImmutableInt(int value) { this.value = value; } public int getValue() { return value; } } public class TestImmutableSpeed extends SimpleBenchmark { static long testMutable(final int arrLen) { MutableInt[] arrMutable = new MutableInt[arrLen]; for (int i = 0; i </pre> <p>Output of Caliper:</p> <pre> 0% Scenario{vm=java, trial=0, benchmark=Mutable, type=-server, minMemory=-Xms256m, optimizations=-XX:+AggressiveOpts} 91614044.60 ns; ?=250338.20 ns @ 3 trials 50% Scenario{vm=java, trial=0, benchmark=Immutable, type=-server, minMemory=-Xms256m, optimizations=-XX:+AggressiveOpts} 1108057922.00 ns; ?=3920760.98 ns @ 3 trials benchmark ms linear runtime Mutable 91.6 == Immutable 1108.1 ============================== </pre> <p>Note that without the optimization parameters for JVM output of Caliper is:</p> <pre> 0% Scenario{vm=java, trial=0, benchmark=Mutable} 516562214.00 ns; ?=623120.57 ns @ 3 trials 50% Scenario{vm=java, trial=0, benchmark=Immutable} 1706758503.00 ns; ?=5842389.60 ns @ 3 trials benchmark ms linear runtime Mutable 517 ========= Immutable 1707 ============================== </pre> <p>So bad parameters make both version slow, but ratio is less terrible (but not important, still).</p>
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    1. COYeah, thats why I use [mutable wrappers](http://jodd.org/api/jodd/mutable/package-summary.html) when speed matters (like, always:) and when I need to have objects. 3x ratio is more likely then 15x.
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    2. COI hadn't seen that there was more code - scroll areas inside scroll areas. Makes more sense. You can't really have microbenchmarking switching between algorithms like that. Not even if you do it several times in a big loop.
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    3. CO@ɹoƃı ratio is (cost of allocation) / (speed of calculation per allocation), so it changes with algorithm and class. For this particular case, speed of "int" is super fast for calculation, and ratio is higher.
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