Scala
code:
@annotation.tailrec
private def fastLoop(n: Int, a: Long = 0, b: Long = 1): Long =
if (n > 1) fastLoop(n - 1, b, a + b) else b
bytecode:
private long fastLoop(int, long, long);
Code:
0: iload_1
1: iconst_1
2: if_icmple 21
5: iload_1
6: iconst_1
7: isub
8: lload 4
10: lload_2
11: lload 4
13: ladd
14: lstore 4
16: lstore_2
17: istore_1
18: goto 0
21: lload 4
23: lreturn
result is 53879289.462 ± 6289454.961 ops/s:
https://travis-ci.org/plokhotnyuk/scala-vs-java/jobs/56117116#L2909
Java
code:
private long fastLoop(int n, long a, long b) {
while (n > 1) {
long c = a + b;
a = b;
b = c;
n--;
}
return b;
}
bytecode:
private long fastLoop(int, long, long);
Code:
0: iload_1
1: iconst_1
2: if_icmple 24
5: lload_2
6: lload 4
8: ladd
9: lstore 6
11: lload 4
13: lstore_2
14: lload 6
16: lstore 4
18: iinc 1, -1
21: goto 0
24: lload 4
26: lreturn
result is 17444340.812 ± 9508030.117 ops/s:
https://travis-ci.org/plokhotnyuk/scala-vs-java/jobs/56117116#L2881
Yes, it depends on environment parameters (JDK version, CPU model & frequency of RAM) and dynamic state. But why mostly the same bytecode on the same environment can produce stable 2x-3x difference for range of function arguments?
Here is list of ops/s numbers for different values of function arguments from my notebook with Intel(R) Core(TM) i7-2640M CPU @ 2.80GHz (max 3.50GHz), RAM 12Gb DDR3-1333, Ubuntu 14.10, Oracle JDK 1.8.0_40-b25 64-bit:
[info] Benchmark (n) Mode Cnt Score Error Units
[info] JavaFibonacci.loop 2 thrpt 5 171776163.027 ± 4620419.353 ops/s
[info] JavaFibonacci.loop 4 thrpt 5 144793748.362 ± 25506649.671 ops/s
[info] JavaFibonacci.loop 8 thrpt 5 67271848.598 ± 15133193.309 ops/s
[info] JavaFibonacci.loop 16 thrpt 5 54552795.336 ± 17398924.190 ops/s
[info] JavaFibonacci.loop 32 thrpt 5 41156886.101 ± 12905023.289 ops/s
[info] JavaFibonacci.loop 64 thrpt 5 24407771.671 ± 4614357.030 ops/s
[info] ScalaFibonacci.loop 2 thrpt 5 148926292.076 ± 23673126.125 ops/s
[info] ScalaFibonacci.loop 4 thrpt 5 139184195.527 ± 30616384.925 ops/s
[info] ScalaFibonacci.loop 8 thrpt 5 109050091.514 ± 23506756.224 ops/s
[info] ScalaFibonacci.loop 16 thrpt 5 81290743.288 ± 5214733.740 ops/s
[info] ScalaFibonacci.loop 32 thrpt 5 38937420.431 ± 8324732.107 ops/s
[info] ScalaFibonacci.loop 64 thrpt 5 22641295.988 ± 5961435.507 ops/s
Additional question is "why values of ops/s are decreasing in non-linear way as above?"
Yes, I was wrong, and missed that tested method was not just fastLoop calls:
Scala
@Benchmark
def loop(): BigInt =
if (n > 92) loop(n - 91, 4660046610375530309L, 7540113804746346429L)
else fastLoop(n)
Java
@Benchmark
public BigInteger loop() {
return n > 92 ?
loop(n - 91, BigInteger.valueOf(4660046610375530309L), BigInteger.valueOf(7540113804746346429L)) :
BigInteger.valueOf(fastLoop(n, 0, 1));
}
As Aleksey noted lot of time was spend in conversions from Long/long to BigInt/BigInteger.
I have wrote separate benchmarks which tests just fastLoop(n, 0, 1) call. Here are results from them:
[info] JavaFibonacci.fastLoop 2 thrpt 5 338071686.910 ± 66146042.535 ops/s
[info] JavaFibonacci.fastLoop 4 thrpt 5 231066635.073 ± 3702419.585 ops/s
[info] JavaFibonacci.fastLoop 8 thrpt 5 174832245.690 ± 36491363.939 ops/s
[info] JavaFibonacci.fastLoop 16 thrpt 5 95162799.968 ± 16151609.596 ops/s
[info] JavaFibonacci.fastLoop 32 thrpt 5 60197918.766 ± 10662747.434 ops/s
[info] JavaFibonacci.fastLoop 64 thrpt 5 29564087.602 ± 3610164.011 ops/s
[info] ScalaFibonacci.fastLoop 2 thrpt 5 336588218.560 ± 56762496.725 ops/s
[info] ScalaFibonacci.fastLoop 4 thrpt 5 224918874.670 ± 35499107.133 ops/s
[info] ScalaFibonacci.fastLoop 8 thrpt 5 121952667.394 ± 17314931.711 ops/s
[info] ScalaFibonacci.fastLoop 16 thrpt 5 96573968.960 ± 12757890.175 ops/s
[info] ScalaFibonacci.fastLoop 32 thrpt 5 59462408.940 ± 14924369.138 ops/s
[info] ScalaFibonacci.fastLoop 64 thrpt 5 28922994.377 ± 7209467.197 ops/s
Lessons that I learned:
Scala implicits can eat lot of performance, while are easy to be overlooked;
Cashing of BigInt values in Scala can speed up some functions comparing with Java's BigInteger.
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