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POFalse Sharing only became noticeable on certain machines
 

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  1. POFalse Sharing only became noticeable on certain machines
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    <p>I wrote the following test class in java to reproduce the performance penalty introduced by "False Sharing".</p> <p>Basically you can tweak the "size" of array from 4 to a much larger value (e.g. 10000) to turn the "False Sharing phenomenon" either on or off. To be specific, when size = 4, different threads are more likely to update values within the same cache line, causing much more frequent cache misses. In theory, the test program should run much faster when size = 10000 than size = 4.</p> <p>I ran the same test on two different machines multiple times:</p> <p><strong>Machine A:</strong> Lenovo X230 laptop w/ Intel® Core™ i5-3210M Processor (2 core, 4 threads) Windows 7 64bit</p> <p>size = 4 => 5.5 second</p> <p>size = 10000 => 5.4 second</p> <p><strong>Machine B:</strong> Dell OptiPlex 780 w/ Intel® Core™2 Duo Processor E8400 (2 core) Windows XP 32bit</p> <p>size = 4 => 14.5 second</p> <p>size = 10000 => 7.2 second</p> <p>I ran the tests later on a few other machines and quite obviously False Sharing only becomes noticeable on certain machines and I couldn't figure out the decisive factor that makes such difference.</p> <p>Can anyone kindly take a look at this problem and explain why false sharing introduced in this test class only became noticeable on certain machines?</p> <pre><code>public class FalseSharing { interface Oper { int eval(int value); } //try tweak the size static int size = 4; //try tweak the op static Oper op = new Oper() { @Override public int eval(int value) { return value + 2; } }; static int[] array = new int[10000 + size]; static final int interval = (size / 4); public static void main(String args[]) throws InterruptedException { long start = System.currentTimeMillis(); Thread t1 = new Thread(new Runnable() { @Override public void run() { System.out.println("Array index:" + 5000); for (int j = 0; j &lt; 30; j++) { for (int i = 0; i &lt; 1000000000; i++) { array[5000] = op.eval(array[5000]); } } } }); Thread t2 = new Thread(new Runnable() { @Override public void run() { System.out.println("Array index:" + (5000 + interval)); for (int j = 0; j &lt; 30; j++) { for (int i = 0; i &lt; 1000000000; i++) { array[5000 + interval] = op.eval(array[5000 + interval]); } } } }); Thread t3 = new Thread(new Runnable() { @Override public void run() { System.out.println("Array index:" + (5000 + interval * 2)); for (int j = 0; j &lt; 30; j++) { for (int i = 0; i &lt; 1000000000; i++) { array[5000 + interval * 2] = op.eval(array[5000 + interval * 2]); } } } }); Thread t4 = new Thread(new Runnable() { @Override public void run() { System.out.println("Array index:" + (5000 + interval * 3)); for (int j = 0; j &lt; 30; j++) { for (int i = 0; i &lt; 1000000000; i++) { array[5000 + interval * 3] = op.eval(array[5000 + interval * 3]); } } } }); t1.start(); t2.start(); t3.start(); t4.start(); t1.join(); t2.join(); t3.join(); t4.join(); System.out.println("Finished!" + (System.currentTimeMillis() - start)); } </code></pre> <p>}</p>
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    1. COArchitectural differences can change the result. Mobile processors are designed differently to save power and don't perform as fast in the first place. A drop of 2x is pretty small, you can get much worse slow down due to false sharing.
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      1. POFalse Sharing only became noticeable on certain machines
      1. USPeter Lawrey
    2. CO@PeterLawrey In this case the mobile processor (i5-3210M) shows no drop on performance:)
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