Explore quicksort with numba

Pipfile

[packages]
numba = "~=0.55"
matplotlib = "~=3.5"

[requires]
python_version = "3.9"

Pipfile.lock

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quicksort.py

import random

import matplotlib.pyplot as plt
import numba as nb
import numpy as np

from collections import defaultdict
from dataclasses import dataclass
from time import time

from numba.typed import List

@dataclass
class Statistics:
    data: list[float]
    _mean: float = None
    _stdev: float = None

    @property
    def mean(self):
        if self._mean is None:
            self._mean = np.mean(self.data)
        return self._mean

    @property
    def stdev(self):
        if self._stdev is None:
            self._stdev = np.std(self.data)
        return self._stdev

class Timer:
    
    def __init__(self, n, result_check, n_warmup=1, ):
        self.n_warmup = n_warmup
        self.n = n
        self.result_check = result_check
        

    def time(self, func):
        result = None
        times = []
        for _ in range(self.n_warmup):
            result = func()

        for _ in range(self.n):
            t0 = time()
            result = func()
            t1 = time()
            times.append(t1 - t0)

        assert self.result_check(result)
        return Statistics(times)

   
def partition(l: list[float], low: int, high: int) -> int:
    pivot = l[high]
    idx = low - 1

    for k in range(low, high):
        tmp = l[k]
        if tmp <= pivot:
            idx += 1

            # swap
            l[k] = l[idx]
            l[idx] = tmp

    idx += 1
    tmp = l[idx]
    l[idx] = l[high]
    l[high] = tmp

    return idx

partition_signature=[
    nb.int64(nb.typeof(List([1.0])), nb.int64, nb.int64),
    nb.int64(nb.typeof(np.asarray([1.0])), nb.int64, nb.int64)
]
@nb.jit(partition_signature, nopython=True)
def partition_jit(l: List, low: int, high: int) -> int:
    pivot = l[high]
    idx = low - 1

    for k in range(low, high):
        tmp = l[k]
        if tmp <= pivot:
            idx += 1

            # swap
            l[k] = l[idx]
            l[idx] = tmp

    idx += 1
    tmp = l[idx]
    l[idx] = l[high]
    l[high] = tmp

    return idx

def quick_sort_inplace(l: List[float]):
    quick_sort_inplace_impl(l, 0, len(l) - 1)

def quick_sort_inplace_impl(l: List[float], low: int, high: int):
    if low >= high or low < 0:
        return

    p = partition(l, low, high)
    quick_sort_inplace_impl(l, low, p - 1)
    quick_sort_inplace_impl(l, p + 1, high)


def quick_sort_inplace_jit(l: List[float]):
    quick_sort_inplace_jit_impl(l, 0, len(l) - 1)
   

impl_signature=[
    nb.void(nb.typeof(List([1.0])), nb.int64, nb.int64),
    nb.void(nb.typeof(np.asarray([1.0])), nb.int64, nb.int64),
]
@nb.jit(impl_signature, nopython=True)
def quick_sort_inplace_jit_impl(l: List[float], low: int, high: int):

    if low >= high or low < 0:
        return

    p = partition_jit(l, low, high)
    quick_sort_inplace_jit_impl(l, low, p - 1)
    quick_sort_inplace_jit_impl(l, p + 1, high)
   
   
def sort_with(data, sorter, reference):
    sorter(data)
    return data


def as_numba_list(l: list) -> List:
    nl = List()
    for el in l:
        nl.append(el)
    return nl

ns = (10, 100, 1_000, 10_000, 100_000, 1_000_000, 10_000_000)
runtimes = defaultdict(list)
for n in ns:

    random.seed(42)
    data = [random.random() for _ in range(n)]
    ground_truth = sorted(data)

    t = Timer(2, lambda x: list(x) == ground_truth)

    runtimes['list.sort(timsort)'].append(t.time(lambda: sort_with(list(data), sorter=lambda x: x.sort(), reference=ground_truth)))
    runtimes['quicksort-jit(nb.typed.List)'].append(t.time(lambda: sort_with(as_numba_list(data), sorter=quick_sort_inplace_jit, reference=ground_truth)))
    runtimes['quicksort-jit(np.ndarray)'].append(t.time(lambda: sort_with(np.asarray(data), sorter=quick_sort_inplace_jit, reference=ground_truth)))
    runtimes['ndarray.sort(quicksort)'].append(t.time(lambda: sort_with(np.asarray(data), sorter=lambda x: x.sort(kind='quicksort'), reference=ground_truth)))
    runtimes['quicksort(list)'].append(t.time(lambda: sort_with(list(data), sorter=quick_sort_inplace, reference=ground_truth)))
    runtimes['quicksort(np.ndarray)'].append(t.time(lambda: sort_with(np.asarray(data), sorter=quick_sort_inplace, reference=ground_truth)))

for k, v in sorted(runtimes.items()):
    plt.plot(ns, [rt.mean for rt in v], label=k)#, yerr=[rt.stdev for rt in v])

plt.legend()
plt.xscale('log')
plt.yscale('log')
plt.show()

runtimes.pdf