Tuesday, September 19, 2017

The numba python module - part 002 .

Today I tested how fast is jit from numba python and fibonacci math function.
You will see strange output I got for some values.
First example:
import numba
from numba import jit
from timeit import default_timer as timer

def fibonacci(n):
    a, b = 1, 1
    for i in range(n):
        a, b = a+b, a
    return a
fibonacci_jit = jit(fibonacci)

start = timer()
fibonacci(100)
duration = timer() - start

startnext = timer()
fibonacci_jit(100)
durationnext = timer() - startnext

print(duration, durationnext)
The result of this run is:
C:\Python27>python numba_test_003.py
(0.00018731270733896962, 0.167499256682878)

C:\Python27>python numba_test_003.py
(1.6357787798437412e-05, 0.1683614083221368)

C:\Python27>python numba_test_003.py
(2.245186560569841e-05, 0.1758382003097716)

C:\Python27>python numba_test_003.py
(2.3093347480146938e-05, 0.16714964906130353)

C:\Python27>python numba_test_003.py
(1.5395564986764625e-05, 0.17471143739730277)

C:\Python27>python numba_test_003.py
(1.5074824049540363e-05, 0.1847134227837042)
As you can see the fibonacci function is not very fast.
The jit - just-in-time compile is very fast.
Let's see if the python source code may slow down.
Let's see the new source code with jit will not work well:
import numba
from numba import jit
from timeit import default_timer as timer

def fibonacci(n):
    a, b = 1, 1
    for i in range(n):
        a, b = a+b, a
    return a
fibonacci_jit = jit(fibonacci)

start = timer()
print fibonacci(100)
duration = timer() - start

startnext = timer()
print fibonacci_jit(100)
durationnext = timer() - startnext

print(duration, durationnext)
The result is this:
C:\Python27>python numba_test_003.py
927372692193078999176
1445263496
(0.0002334994022992635, 0.17628787910376)

C:\Python27>python numba_test_003.py
927372692193078999176
1445263496
(0.0006886307922204926, 0.17579169287387408)

C:\Python27>python numba_test_003.py
927372692193078999176
1445263496
(0.0008105123483657127, 0.18209553525407973)

C:\Python27>python numba_test_003.py
927372692193078999176
1445263496
(0.00025466830415606486, 0.17186550306131188)

C:\Python27>python numba_test_003.py
927372692193078999176
1445263496
(0.0007348174871807866, 0.17523103771560608)
The result for value 100 is not the same: 927372692193078999176 and 1445263496.
First problem is:
The problem is that numba can't intuit the type of lookup. If you put a print nb.typeof(lookup) in your method, you'll see that numba is treating it as an object, which is slow.
The second problem is the output but can be from same reason.
I test with value 5 and the result is :
C:\Python27>python numba_test_003.py
13
13
13
13
(0.0007258367409385072, 0.17057997338491704)

C:\Python27>python numba_test_003.py
13
13
(0.00033709872502270044, 0.17213235952108247)

C:\Python27>python numba_test_003.py
13
13
(0.0004836773333341886, 0.17184433415945508)

C:\Python27>python numba_test_003.py
13
13
(0.0006854233828482501, 0.17381272129120037)