Why is 2 i i faster than 2 i i in Java

Question

The following Java program takes on average between 0 50 secs and 0 55 secs to run   public static void main String   args        long startTime   System nanoTime        int n   0      for  int i   0  i  lt  1000000000  i              n    2    i   i             System out println  double   System nanoTime   - startTime    1000000000     s        System out println  n       n       If I replace 2    i   i  with 2   i   i  it takes between 0 60 and 0 65 secs to run  How come   I ran each version of the program 15 times  alternating between the two  Here are the results    2  i i      2 i i ---------- ---------- 0 5183738   0 6246434 0 5298337   0 6049722 0 5308647   0 6603363 0 5133458   0 6243328 0 5003011   0 6541802 0 5366181   0 6312638 0 515149    0 6241105 0 5237389   0 627815 0 5249942   0 6114252 0 5641624   0 6781033 0 538412    0 6393969 0 5466744   0 6608845 0 531159    0 6201077 0 5048032   0 6511559 0 5232789   0 6544526   The fastest run of 2   i   i took longer than the slowest run of 2    i   i   If they had the same efficiency  the probability of this happening would be less than 1 2 15   100    0 00305

User · Answer

I tried a JMH using the default archetype: I also added an optimized version based on Runemoro's explanation.

@State(Scope.Benchmark)
@Warmup(iterations = 2)
@Fork(1)
@Measurement(iterations = 10)
@OutputTimeUnit(TimeUnit.NANOSECONDS)
//@BenchmarkMode({ Mode.All })
@BenchmarkMode(Mode.AverageTime)
public class MyBenchmark {
  @Param({ "100", "1000", "1000000000" })
  private int size;

  @Benchmark
  public int two_square_i() {
    int n = 0;
    for (int i = 0; i < size; i++) {
      n += 2 * (i * i);
    }
    return n;
  }

  @Benchmark
  public int square_i_two() {
    int n = 0;
    for (int i = 0; i < size; i++) {
      n += i * i;
    }
    return 2*n;
  }

  @Benchmark
  public int two_i_() {
    int n = 0;
    for (int i = 0; i < size; i++) {
      n += 2 * i * i;
    }
    return n;
  }
}

The result are here:

Benchmark                           (size)  Mode  Samples          Score   Score error  Units
o.s.MyBenchmark.square_i_two           100  avgt       10         58,062         1,410  ns/op
o.s.MyBenchmark.square_i_two          1000  avgt       10        547,393        12,851  ns/op
o.s.MyBenchmark.square_i_two    1000000000  avgt       10  540343681,267  16795210,324  ns/op
o.s.MyBenchmark.two_i_                 100  avgt       10         87,491         2,004  ns/op
o.s.MyBenchmark.two_i_                1000  avgt       10       1015,388        30,313  ns/op
o.s.MyBenchmark.two_i_          1000000000  avgt       10  967100076,600  24929570,556  ns/op
o.s.MyBenchmark.two_square_i           100  avgt       10         70,715         2,107  ns/op
o.s.MyBenchmark.two_square_i          1000  avgt       10        686,977        24,613  ns/op
o.s.MyBenchmark.two_square_i    1000000000  avgt       10  652736811,450  27015580,488  ns/op

On my PC (Core i7 860 - it is doing nothing much apart from reading on my smartphone):

n += i*i then n*2 is first
2 * (i * i) is second.

The JVM is clearly not optimizing the same way than a human does (based on Runemoro's answer).

Now then, reading bytecode: javap -c -v ./target/classes/org/sample/MyBenchmark.class

Differences between 2*(i*i) (left) and 2*i*i (right) here: https://www.diffchecker.com/cvSFppWI
Differences between 2*(i*i) and the optimized version here: https://www.diffchecker.com/I1XFu5dP

I am not expert on bytecode, but we iload_2 before we imul: that's probably where you get the difference: I can suppose that the JVM optimize reading i twice (i is already here, and there is no need to load it again) whilst in the 2*i*i it can't.

User · Answer

I got similar results   2    i   i   0 458765943 s  n 119860736 2   i   i  0 580255126 s  n 119860736   I got the SAME results if both loops were in the same program  or each was in a separate  java file  class  executed on a separate run   Finally  here is a javap -c -v  lt  java gt  decompile of each        3  ldc            3                     String 2    i   i        5  invokevirtual  4                     Method java io PrintStream print  Ljava lang String  V      8  invokestatic   5                     Method java lang System nanoTime   J      8  invokestatic   5                     Method java lang System nanoTime   J     11  lstore 1     12  iconst 0     13  istore 3     14  iconst 0     15  istore        4     17  iload         4     19  ldc            6                     int 1000000000     21  if icmpge     40     24  iload 3     25  iconst 2     26  iload         4     28  iload         4     30  imul     31  imul     32  iadd     33  istore 3     34  iinc          4  1     37  goto          17   vs        3  ldc            3                     String 2   i   i       5  invokevirtual  4                     Method java io PrintStream print  Ljava lang String  V      8  invokestatic   5                     Method java lang System nanoTime   J     11  lstore 1     12  iconst 0     13  istore 3     14  iconst 0     15  istore        4     17  iload         4     19  ldc            6                     int 1000000000     21  if icmpge     40     24  iload 3     25  iconst 2     26  iload         4     28  imul     29  iload         4     31  imul     32  iadd     33  istore 3     34  iinc          4  1     37  goto          17   FYI -  java -version java version  1 8 0 121  Java TM  SE Runtime Environment  build 1 8 0 121-b13  Java HotSpot TM  64-Bit Server VM  build 25 121-b13  mixed mode

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Editor s note  this answer is contradicted by evidence from looking at the asm  as shown by another answer   This was a guess backed up by some experiments  but it turned out not to be correct    When the multiplication is 2    i   i   the JVM is able to factor out the multiplication by 2 from the loop  resulting in this equivalent but more efficient code  int n   0  for  int i   0  i  lt  1000000000  i          n    i   i    n    2   but when the multiplication is  2   i    i  the JVM doesn t optimize it since the multiplication by a constant is no longer right before the n    addition  Here are a few reasons why I think this is the case   Adding an if  n    0  n   1 statement at the start of the loop results in both versions being as efficient  since factoring out the multiplication no longer guarantees that the result will be the same The optimized version  by factoring out the multiplication by 2  is exactly as fast as the 2    i   i  version  Here is the test code that I used to draw these conclusions  public static void main String   args        long fastVersion   0      long slowVersion   0      long optimizedVersion   0      long modifiedFastVersion   0      long modifiedSlowVersion   0       for  int i   0  i  lt  10  i              fastVersion    fastVersion            slowVersion    slowVersion            optimizedVersion    optimizedVersion            modifiedFastVersion    modifiedFastVersion            modifiedSlowVersion    modifiedSlowVersion               System out println  quot Fast version   quot     double  fastVersion   1000000000    quot  s quot        System out println  quot Slow version   quot     double  slowVersion   1000000000    quot  s quot        System out println  quot Optimized version   quot     double  optimizedVersion   1000000000    quot  s quot        System out println  quot Modified fast version   quot     double  modifiedFastVersion   1000000000    quot  s quot        System out println  quot Modified slow version   quot     double  modifiedSlowVersion   1000000000    quot  s quot       private static long fastVersion         long startTime   System nanoTime        int n   0      for  int i   0  i  lt  1000000000  i              n    2    i   i             return System nanoTime   - startTime     private static long slowVersion         long startTime   System nanoTime        int n   0      for  int i   0  i  lt  1000000000  i              n    2   i   i            return System nanoTime   - startTime     private static long optimizedVersion         long startTime   System nanoTime        int n   0      for  int i   0  i  lt  1000000000  i              n    i   i            n    2      return System nanoTime   - startTime     private static long modifiedFastVersion         long startTime   System nanoTime        int n   0      for  int i   0  i  lt  1000000000  i              if  n    0  n   1          n    2    i   i             return System nanoTime   - startTime     private static long modifiedSlowVersion         long startTime   System nanoTime        int n   0      for  int i   0  i  lt  1000000000  i              if  n    0  n   1          n    2   i   i            return System nanoTime   - startTime     And here are the results  Fast version  5 7274411 s Slow version  7 6190804 s Optimized version  5 1348007 s Modified fast version  7 1492705 s Modified slow version  7 2952668 s

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The two methods of adding do generate slightly different byte code     17  iconst 2   18  iload         4   20  iload         4   22  imul   23  imul   24  iadd   For 2    i   i  vs     17  iconst 2   18  iload         4   20  imul   21  iload         4   23  imul   24  iadd   For 2   i   i   And when using a JMH benchmark like this    Warmup iterations   5  batchSize   1   Measurement iterations   5  batchSize   1   Fork 1   BenchmarkMode Mode AverageTime   OutputTimeUnit TimeUnit MILLISECONDS   State Scope Benchmark  public class MyBenchmark         Benchmark     public int noBrackets             int n   0          for  int i   0  i  lt  1000000000  i                  n    2   i   i                    return n              Benchmark     public int brackets             int n   0          for  int i   0  i  lt  1000000000  i                  n    2    i   i                     return n             The difference is clear     JMH version  1 21   VM version  JDK 11  Java HotSpot TM  64-Bit Server VM  11 28   VM options   lt none gt   Benchmark                       n   Mode  Cnt    Score    Error  Units MyBenchmark brackets    1000000000  avgt    5  380 889    58 011  ms op MyBenchmark noBrackets  1000000000  avgt    5  512 464    11 098  ms op   What you observe is correct  and not just an anomaly of your benchmarking style  i e  no warmup  see How do I write a correct micro-benchmark in Java    Running again with Graal     JMH version  1 21   VM version  JDK 11  Java HotSpot TM  64-Bit Server VM  11 28   VM options  -XX  UnlockExperimentalVMOptions -XX  EnableJVMCI -XX  UseJVMCICompiler  Benchmark                       n   Mode  Cnt    Score    Error  Units MyBenchmark brackets    1000000000  avgt    5  335 100    23 085  ms op MyBenchmark noBrackets  1000000000  avgt    5  331 163    50 670  ms op   You see that the results are much closer  which makes sense  since Graal is an overall better performing  more modern  compiler   So this is really just up to how well the JIT compiler is able to optimize a particular piece of code  and doesn t necessarily have a logical reason to it

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Interesting observation using Java 11 and switching off loop unrolling with the following VM option   -XX LoopUnrollLimit 0   The loop with the 2    i   i  expression results in more compact native code1   L0001  add    eax r11d        inc    r8d        mov    r11d r8d        imul   r11d r8d        shl    r11d 1h        cmp    r8d r10d        jl     L0001   in comparison with the 2   i   i version   L0001  add    eax r11d        mov    r11d r8d        shl    r11d 1h        add    r11d 2h        inc    r8d        imul   r11d r8d        cmp    r8d r10d        jl     L0001   Java version   java version  11  2018-09-25 Java TM  SE Runtime Environment 18 9  build 11 28  Java HotSpot TM  64-Bit Server VM 18 9  build 11 28  mixed mode    Benchmark results   Benchmark           size   Mode  Cnt    Score     Error  Units LoopTest fast  1000000000  avgt    5  694 868     36 470  ms op LoopTest slow  1000000000  avgt    5  769 840    135 006  ms op   Benchmark source code    BenchmarkMode Mode AverageTime   OutputTimeUnit TimeUnit MILLISECONDS   Warmup iterations   5  time   5  timeUnit   TimeUnit SECONDS   Measurement iterations   5  time   5  timeUnit   TimeUnit SECONDS   State Scope Thread   Fork 1  public class LoopTest         Param  1000000000   private int size       public static void main String   args  throws RunnerException           Options opt   new OptionsBuilder                include LoopTest class getSimpleName                 jvmArgs  -XX LoopUnrollLimit 0                build            new Runner opt  run                Benchmark     public int slow             int n   0          for  int i   0  i  lt  size  i                n    2   i   i          return n              Benchmark     public int fast             int n   0          for  int i   0  i  lt  size  i                n    2    i   i           return n              1 - VM options used  -XX  UnlockDiagnosticVMOptions -XX  PrintAssembly -XX LoopUnrollLimit 0

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While not directly related to the question s environment  just for the curiosity  I did the same test on  NET Core 2 1  x64  release mode   Here is the interesting result  confirming similar phonomena  other way around  happening over the dark side of the force  Code   static void Main string   args        Stopwatch watch   new Stopwatch         Console WriteLine  2    i   i          for  int a   0  a  lt  10  a                  int n   0           watch Restart             for  int i   0  i  lt  1000000000  i                          n    2    i   i                      watch Stop             Console WriteLine   result  n    watch ElapsedMilliseconds  ms               Console WriteLine        Console WriteLine  2   i   i         for  int a   0  a  lt  10  a                  int n   0           watch Restart             for  int i   0  i  lt  1000000000  i                          n    2   i   i                     watch Stop             Console WriteLine   result  n    watch ElapsedMilliseconds ms              Result   2    i   i    result 119860736  438 ms result 119860736  433 ms result 119860736  437 ms result 119860736  435 ms result 119860736  436 ms result 119860736  435 ms result 119860736  435 ms result 119860736  439 ms result 119860736  436 ms result 119860736  437 ms   2   i   i   result 119860736  417 ms result 119860736  417 ms result 119860736  417 ms result 119860736  418 ms result 119860736  418 ms result 119860736  417 ms result 119860736  418 ms result 119860736  416 ms result 119860736  417 ms result 119860736  418 ms

User · Answer

Kasperd asked in a comment of the accepted answer      The Java and C examples use quite different register names  Are both example using the AMD64 ISA    xor edx  edx xor eax  eax  L2  mov ecx  edx imul ecx  edx add edx  1 lea eax   rax rcx 2  cmp edx  1000000000 jne  L2   I don t have enough reputation to answer this in the comments  but these are the same ISA  It s worth pointing out that the GCC version uses 32-bit integer logic and the JVM compiled version uses 64-bit integer logic internally   R8 to R15 are just new X86 64 registers  EAX to EDX are the lower parts of the RAX to RDX general purpose registers  The important part in the answer is that the GCC version is not unrolled  It simply executes one round of the loop per actual machine code loop  While the JVM version has 16 rounds of the loop in one physical loop  based on rustyx answer  I did not reinterpret the assembly   This is one of the reasons why there are more registers being used since the loop body is actually 16 times longer

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There is a slight difference in the ordering of the bytecode  2    i   i        iconst 2      iload0      iload0      imul      imul      iadd  vs 2   i   i       iconst 2      iload0      imul      iload0      imul      iadd  At first sight this should not make a difference  if anything the second version is more optimal since it uses one slot less  So we need to dig deeper into the lower level  JIT 1  Remember that JIT tends to unroll small loops very aggressively  Indeed we observe a 16x unrolling for the 2    i   i  case  030   B2    B2 B3  lt - B1 B2  Loop  B2-B2 inner main of N18 Freq  1e 006 030     addl    R11  RBP      int 033     movl    RBP  R13      spill 036     addl    RBP   14      int 039     imull   RBP  RBP      int 03c     movl    R9  R13   spill 03f     addl    R9   13   int 043     imull   R9  R9    int 047     sall    RBP   1 049     sall    R9   1 04c     movl    R8  R13   spill 04f     addl    R8   15   int 053     movl    R10  R8   spill 056     movdl   XMM1  R8      spill 05b     imull   R10  R8   int 05f     movl    R8  R13   spill 062     addl    R8   12   int 066     imull   R8  R8    int 06a     sall    R10   1 06d     movl     rsp    32   R10      spill 072     sall    R8   1 075     movl    RBX  R13      spill 078     addl    RBX   11      int 07b     imull   RBX  RBX      int 07e     movl    RCX  R13      spill 081     addl    RCX   10      int 084     imull   RCX  RCX      int 087     sall    RBX   1 089     sall    RCX   1 08b     movl    RDX  R13      spill 08e     addl    RDX   8   int 091     imull   RDX  RDX      int 094     movl    RDI  R13      spill 097     addl    RDI   7   int 09a     imull   RDI  RDI      int 09d     sall    RDX   1 09f     sall    RDI   1 0a1     movl    RAX  R13      spill 0a4     addl    RAX   6   int 0a7     imull   RAX  RAX      int 0aa     movl    RSI  R13      spill 0ad     addl    RSI   4   int 0b0     imull   RSI  RSI      int 0b3     sall    RAX   1 0b5     sall    RSI   1 0b7     movl    R10  R13      spill 0ba     addl    R10   2   int 0be     imull   R10  R10      int 0c2     movl    R14  R13      spill 0c5     incl    R14   int 0c8     imull   R14  R14      int 0cc     sall    R10   1 0cf     sall    R14   1 0d2     addl    R14  R11      int 0d5     addl    R14  R10      int 0d8     movl    R10  R13      spill 0db     addl    R10   3   int 0df     imull   R10  R10      int 0e3     movl    R11  R13      spill 0e6     addl    R11   5   int 0ea     imull   R11  R11      int 0ee     sall    R10   1 0f1     addl    R10  R14      int 0f4     addl    R10  RSI      int 0f7     sall    R11   1 0fa     addl    R11  R10      int 0fd     addl    R11  RAX      int 100     addl    R11  RDI      int 103     addl    R11  RDX      int 106     movl    R10  R13      spill 109     addl    R10   9   int 10d     imull   R10  R10      int 111     sall    R10   1 114     addl    R10  R11      int 117     addl    R10  RCX      int 11a     addl    R10  RBX      int 11d     addl    R10  R8   int 120     addl    R9  R10   int 123     addl    RBP  R9   int 126     addl    RBP   RSP    32  32-bit       int 12a     addl    R13   16      int 12e     movl    R11  R13      spill 131     imull   R11  R13      int 135     sall    R11   1 138     cmpl    R13   999999985 13f     jl     B2     loop end  P 1 000000 C 6554623 000000  We see that there is 1 register that is  quot spilled quot  onto the stack  And for the 2   i   i version  05a   B3    B2 B4  lt - B1 B2  Loop  B3-B2 inner main of N18 Freq  1e 006 05a     addl    RBX  R11      int 05d     movl     rsp    32   RBX      spill 061     movl    R11  R8   spill 064     addl    R11   15      int 068     movl     rsp    36   R11      spill 06d     movl    R11  R8   spill 070     addl    R11   14      int 074     movl    R10  R9   spill 077     addl    R10   16      int 07b     movdl   XMM2  R10     spill 080     movl    RCX  R9   spill 083     addl    RCX   14      int 086     movdl   XMM1  RCX     spill 08a     movl    R10  R9   spill 08d     addl    R10   12      int 091     movdl   XMM4  R10     spill 096     movl    RCX  R9   spill 099     addl    RCX   10      int 09c     movdl   XMM6  RCX     spill 0a0     movl    RBX  R9   spill 0a3     addl    RBX   8   int 0a6     movl    RCX  R9   spill 0a9     addl    RCX   6   int 0ac     movl    RDX  R9   spill 0af     addl    RDX   4   int 0b2     addl    R9   2    int 0b6     movl    R10  R14      spill 0b9     addl    R10   22      int 0bd     movdl   XMM3  R10     spill 0c2     movl    RDI  R14      spill 0c5     addl    RDI   20      int 0c8     movl    RAX  R14      spill 0cb     addl    RAX   32      int 0ce     movl    RSI  R14      spill 0d1     addl    RSI   18      int 0d4     movl    R13  R14      spill 0d7     addl    R13   24      int 0db     movl    R10  R14      spill 0de     addl    R10   26      int 0e2     movl     rsp    40   R10      spill 0e7     movl    RBP  R14      spill 0ea     addl    RBP   28      int 0ed     imull   RBP  R11      int 0f1     addl    R14   30      int 0f5     imull   R14   RSP    36  32-bit       int 0fb     movl    R10  R8   spill 0fe     addl    R10   11      int 102     movdl   R11  XMM3     spill 107     imull   R11  R10      int 10b     movl     rsp    44   R11      spill 110     movl    R10  R8   spill 113     addl    R10   10      int 117     imull   RDI  R10      int 11b     movl    R11  R8   spill 11e     addl    R11   8   int 122     movdl   R10  XMM2     spill 127     imull   R10  R11      int 12b     movl     rsp    48   R10      spill 130     movl    R10  R8   spill 133     addl    R10   7   int 137     movdl   R11  XMM1     spill 13c     imull   R11  R10      int 140     movl     rsp    52   R11      spill 145     movl    R11  R8   spill 148     addl    R11   6   int 14c     movdl   R10  XMM4     spill 151     imull   R10  R11      int 155     movl     rsp    56   R10      spill 15a     movl    R10  R8   spill 15d     addl    R10   5   int 161     movdl   R11  XMM6     spill 166     imull   R11  R10      int 16a     movl     rsp    60   R11      spill 16f     movl    R11  R8   spill 172     addl    R11   4   int 176     imull   RBX  R11      int 17a     movl    R11  R8   spill 17d     addl    R11   3   int 181     imull   RCX  R11      int 185     movl    R10  R8   spill 188     addl    R10   2   int 18c     imull   RDX  R10      int 190     movl    R11  R8   spill 193     incl    R11   int 196     imull   R9  R11   int 19a     addl    R9   RSP    32  32-bit        int 19f     addl    R9  RDX   int 1a2     addl    R9  RCX   int 1a5     addl    R9  RBX   int 1a8     addl    R9   RSP    60  32-bit        int 1ad     addl    R9   RSP    56  32-bit        int 1b2     addl    R9   RSP    52  32-bit        int 1b7     addl    R9   RSP    48  32-bit        int 1bc     movl    R10  R8   spill 1bf     addl    R10   9   int 1c3     imull   R10  RSI      int 1c7     addl    R10  R9   int 1ca     addl    R10  RDI      int 1cd     addl    R10   RSP    44  32-bit       int 1d2     movl    R11  R8   spill 1d5     addl    R11   12      int 1d9     imull   R13  R11      int 1dd     addl    R13  R10      int 1e0     movl    R10  R8   spill 1e3     addl    R10   13      int 1e7     imull   R10   RSP    40  32-bit       int 1ed     addl    R10  R13      int 1f0     addl    RBP  R10      int 1f3     addl    R14  RBP      int 1f6     movl    R10  R8   spill 1f9     addl    R10   16      int 1fd     cmpl    R10   999999985 204     jl     B2     loop end  P 1 000000 C 7419903 000000  Here we observe much more  quot spilling quot  and more accesses to the stack  RSP         due to more intermediate results that need to be preserved  Thus the answer to the question is simple  2    i   i  is faster than 2   i   i because the JIT generates more optimal assembly code for the first case   But of course it is obvious that neither the first nor the second version is any good  the loop could really benefit from vectorization  since any x86-64 CPU has at least SSE2 support  So it s an issue of the optimizer  as is often the case  it unrolls too aggressively and shoots itself in the foot  all the while missing out on various other opportunities  In fact  modern x86-64 CPUs break down the instructions further into micro-ops    ops  and with features like register renaming    op caches and loop buffers  loop optimization takes a lot more finesse than a simple unrolling for optimal performance  According to Agner Fog s optimization guide   The gain in performance due to the   op cache can be quite considerable if the average instruction length is more than 4 bytes  The following methods of optimizing the use of the   op cache may be considered   Make sure that critical loops are small enough to fit into the   op cache  Align the most critical loop entries and function entries by 32  Avoid unnecessary loop unrolling  Avoid instructions that have extra load time         Regarding those load times - even the fastest L1D hit costs 4 cycles  an extra register and   op  so yes  even a few accesses to memory will hurt performance in tight loops  But back to the vectorization opportunity - to see how fast it can be  we can compile a similar C application with GCC  which outright vectorizes it  AVX2 is shown  SSE2 is similar 2    vmovdqa ymm0  YMMWORD PTR  LC0 rip    vmovdqa ymm3  YMMWORD PTR  LC1 rip    xor eax  eax   vpxor xmm2  xmm2  xmm2  L2    vpmulld ymm1  ymm0  ymm0   inc eax   vpaddd ymm0  ymm0  ymm3   vpslld ymm1  ymm1  1   vpaddd ymm2  ymm2  ymm1   cmp eax  125000000        8 calculations per iteration   jne  L2   vmovdqa xmm0  xmm2   vextracti128 xmm2  ymm2  1   vpaddd xmm2  xmm0  xmm2   vpsrldq xmm0  xmm2  8   vpaddd xmm0  xmm2  xmm0   vpsrldq xmm1  xmm0  4   vpaddd xmm0  xmm0  xmm1   vmovd eax  xmm0   vzeroupper  With run times   SSE  0 24 s  or 2 times as fast  AVX  0 15 s  or 3 times as fast  AVX2  0 08 s  or 5 times as fast    1 To get JIT generated assembly output  get a debug JVM and run with -XX  PrintOptoAssembly 2 The C version is compiled with the -fwrapv flag  which enables GCC to treat signed integer overflow as a two s-complement wrap-around

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More of an addendum  I did repro the experiment using the latest Java 8 JVM from IBM   java version  1 8 0 191  Java TM  2 Runtime Environment  Standard Edition  IBM build 1 8 0 191-b12 26 Oct 2018 18 45 Mac OS X x64 SR5 FP25   Java HotSpot TM  64-Bit Server VM  build 25 191-b12  mixed mode    And this shows very similar results   0 374653912 s n   119860736 0 447778698 s n   119860736    second results using 2   i   i    Interestingly enough  when running on the same machine  but using Oracle Java   Java version  1 8 0 181  Java TM  SE Runtime Environment  build 1 8 0 181-b13  Java HotSpot TM  64-Bit Server VM  build 25 181-b13  mixed mode    results are on average a bit slower   0 414331815 s n   119860736 0 491430656 s n   119860736   Long story short  even the minor version number of HotSpot matter here  as subtle differences within the JIT implementation can have notable effects

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Byte codes  https   cs nyu edu courses fall00 V22 0201-001 jvm2 html Byte codes Viewer  https   github com Konloch bytecode-viewer  On my JDK  Windows nbsp 10 64 bit  1 8 0 65-b17  I can reproduce and explain   public static void main String   args        int repeat   10      long A   0      long B   0      for  int i   0  i  lt  repeat  i              A    test            B    testB               System out println A   repeat     ms        System out println B   repeat     ms        private static long test         int n   0      for  int i   0  i  lt  1000  i              n    multi i             long startTime   System currentTimeMillis        for  int i   0  i  lt  1000000000  i              n    multi i             long ms    System currentTimeMillis   - startTime       System out println ms     ms A     n       return ms      private static long testB         int n   0      for  int i   0  i  lt  1000  i              n    multiB i             long startTime   System currentTimeMillis        for  int i   0  i  lt  1000000000  i              n    multiB i             long ms    System currentTimeMillis   - startTime       System out println ms     ms B     n       return ms     private static int multiB int i        return 2    i   i      private static int multi int i        return 2   i   i      Output       405 ms A 785527736 327 ms B 785527736 404 ms A 785527736 329 ms B 785527736 404 ms A 785527736 328 ms B 785527736 404 ms A 785527736 328 ms B 785527736 410 ms 333 ms   So why  The byte code is this    private static multiB int arg0       2    i   i        lt localVar index 0  name i   desc I  sig null  start L1  end L2 gt        L1            iconst 2          iload0          iload0          imul          imul          ireturn             L2               private static multi int arg0       2   i   i       lt localVar index 0  name i   desc I  sig null  start L1  end L2 gt        L1            iconst 2          iload0          imul          iload0          imul          ireturn             L2               The difference being  With brackets  2    i   i      push const stack push local on stack push local on stack multiply top of stack multiply top of stack   Without brackets  2   i   i     push const stack push local on stack multiply top of stack push local on stack multiply top of stack   Loading all on the stack and then working back down is faster than switching between putting on the stack and operating on it

[java] Why is 2 * (i * i) faster than 2 * i * i in Java?

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