Following implementation of Quick sort in javascript with 10 000 000 random elements:
function swap(arr, i1, i2) {
const temp = arr[i1];
arr[i1] = arr[i2]
arr[i2] = temp;
}
function pivot(arr, l, r) {
let pvt = arr[r];
let i = l - 1;
for (let j = l; j <= r - 1; j ) {
if (arr[j] < pvt) {
i ;
swap(arr, i, j);
}
}
swap(arr, i 1, r);
return i 1;
}
function quick_sort(arr, l, r) {
if (l < r) {
let pvt = pivot(arr, l, r);
quick_sort(arr, l, pvt - 1);
quick_sort(arr, pvt 1, r);
}
}
let arr = Array.from(Array(10000000)).map((_) => Math.random());
console.time("quick_sort");
quick_sort(arr, 0, arr.length - 1);
console.timeEnd("quick_sort");
It takes ≈ 1.5s to sort an array.
When I'm testing the same one in python:
import time
import random
from numpy import random
def quicksort(arr):
qs(arr, 0, len(arr) - 1)
def qs(arr, l, r):
if l >= r:
return
p = partition(arr, l, r)
qs(arr, l, p - 1)
qs(arr, p 1, r)
def partition(arr, l, r):
pivot = arr[r]
i = l - 1
for j in range(l, r):
if arr[j] < pivot:
i = 1
arr[i], arr[j] = arr[j], arr[i]
arr[i 1], arr[r] = arr[r], arr[i 1]
return i 1
array = random.rand(10000000)
start = time.time()
quicksort(array)
print("Seconds: ", time.time() - start)
It takes ≈ 131s to sort an array, if using random.rand(10000000)
And ≈ 60s, if using array = [random.random() for _ in range(10000000)] instead
Where does this difference come from?
CodePudding user response:
It depends on which python interpreter you're using.
I assume you're using cpython 3 which, I believe, has no JIT. You have all the overhead of an interpreted language and no tricks to speed things up.
Most modern JS engines have JIT, so the hot code paths get optimized.
Somewhat relatedly, numpy is faster because the internal code is either C or Fortran, which gets called via bindings.
Here's a longer post on software engineering stackexchange that goes more into they why of native python not being as fast as JS.