Question to JMH and JIT

[Herr Knack]

Hey there,

I currently try to find a mathematical model to compute the approximate
runtime of a function term out of its basic structure for my machine
(Ubuntu 16.04, i7-5500U, 2.40GHz×4). For first microbenchmark measurements
I used the JMH and measured the throughput of some simple functions (4
different forks, 3 warmup and 5 measurement iterations each fork, 10 sec
time each iteration).

I found out, that it is really difficult to see the connection between the
structure of the function term and its runtime in the microbenchmark. I
assume that the JIT compiler does some weird stuff to optimize the
functions, but I can‘t see which optimizations in detail are done there.

Could JIT be the problem? Does anyone know if the workflow of JIT
optimizations is documented anywhere?

I hope someone can help, because I‘m getting crazy about it. Here some
results I got. I converted the throughput (runs/sec) to a average time per
run in ns. The standard deviation is smaller as you might think, so the
average time is most likely correct in an area of +/-0.3 ns :

- (((0.5 * x) * 0.5) * (((((0.5 * x) * 0.5) / 0.5) + (((0.5 * x) * 0.5) *
0.5)) * (((0.5 * x) * 0.5) + x))) + x, 15 operations (3+, 11*, 1/), 28.09 ns

- x + ((((0.5 * x) * (0.5 * x)) * (0.5 * x)) + ((0.5 * x) * ((((0.5 * x) *
(0.5 * x)) * (0.5 * x)) * (((0.5 * x) * (0.5 * x)) / ((((0.5 * x) * ((0.5 *
x) * (0.5 * x))) / (0.5 + ((0.5 * x) * (0.5 * x)))) + (((0.5 * x) * (0.5 *
x)) * (0.5 * x))))))), 35 operations (4+,29*,2/), 38.61 ns

- (((x / (x / (0.5 * x))) * (x / (x / (0.5 * x)))) * (0.5 * x)) + (x +
((((x / (x / (0.5 * x))) * (x / (x / (0.5 * x)))) * (0.5 * x)) / (((x /
(0.5 * x)) + 0.5) - ((((x / (x / (0.5 * x))) * (x / (x / (0.5 * x)))) *
(0.5 * x)) * (x / (x / (0.5 * x))))))), 38 operations (4+-,18*,16/), 40.70
ns

I assume that there is an constant offset for method calls and so on. In
addition I suppose that already calculated term pieces don‘t have to be
calculated again. But these conclusions did not help me either.

Regards,

KKokser