Get began with Python’s new native JIT

Right here’s an instance of a program that demonstrates fairly constant speedups with the JIT enabled. It’s a rudimentary model of the Mandelbroit fractal:

from time import perf_counter
import sys

print ("JIT enabled:", sys._jit.is_enabled())

WIDTH = 80
HEIGHT = 40
X_MIN, X_MAX = -2.0, 1.0
Y_MIN, Y_MAX = -1.0, 1.0
ITERS = 500

YM = (Y_MAX - Y_MIN)
XM = (X_MAX - X_MIN)

def iter(c):
    z = 0j
    for _ in vary(ITERS):
        if abs(z) > 2.0:
            return False
        z = z ** 2 + c
    return True

def generate():
    begin = perf_counter()
    output = []

    for y in vary(HEIGHT):
        cy = Y_MIN + (y / HEIGHT) * YM
        for x in vary(WIDTH):
            cx = X_MIN + (x / WIDTH) * XM
            c = advanced(cx, cy)
            output.append("#" if iter(c) else ".")
        output.append("n")
    print ("Time:", perf_counter()-start)
    return output

print("".be a part of(generate()))

When this system begins operating, it lets you understand if the JIT is enabled after which produces a plot of the fractal to the terminal together with the time taken to compute it.

With the JIT enabled, there’s a reasonably constant 20% speedup between runs. If the efficiency increase isn’t apparent, attempt altering the worth of ITERS to the next quantity. This forces this system to do extra work, so ought to produce a extra apparent speedup.