Looping Over Small List Is Fast Enough -> You Must Quit Coding Right Now

Benchmarking over size of list = [10, 100, 1000, 10000, 100000]

Test Target:

• 0. Dumbest for loop
• 1. Dumbest for loop with indexing
• 2. Pure indexing
• 3. SIMD indexing

Result:

size =  1e1      1e2      1e3      1e4       1e5
0.       1X       1X       1X       1X        1X
1.      10X      61X     520X    5253X    51794X
2.      15X      86X     732X    7287X    70721X
3.     107X    3727X   31271X  279104X  2476500X

And basically you have zero justification for this kind of slowiness.

Imagine the slowest dumbest for loop is everywhere in the server code. For every access or request you have to run that shit once. The server has 100,000 access per day and even 1,000,000 in peak. How much time and energy wasted by you?

%%time

from time import perf_counter as now
import numpy as np
from operator import itemgetter as itg

search_grid = [1,2,3,4,5]
log = [[] for _ in search_grid]
repeat = 10

for i, exponential in enumerate(search_grid):
    test_size = int(10 ** exponential)
    
    loop = 1000
    random_index = np.random.randint(0, test_size, loop)
    container = np.arange(test_size)
    
    t0 = now()
    for _ in range(repeat):
        for idx in random_index:
            for x in container:
                if x == idx:
                    temp = x
    t1 = now()
    
    t10 = now()
    for _ in range(repeat):
        for idx in random_index:
            temp = container[idx]
    t11 = now()
    
    t30 = now()
    for _ in range(repeat):
        temp = itg(*random_index)(container)
    t31 = now()
    
    t20 = now()
    for _ in range(repeat):
        temp = container[random_index]
    t21 = now()
    
    log[i] = [t1-t0, t11-t10, t31-t30, t21-t20]

import matplotlib.pyplot as plt

log_np = np.array(log)/10
log_np = log_np.T
log_np

plt.rcParams.update({'font.size': 18})

x = np.arange(len(search_grid))  # the label locations
width = 0.25  # the width of the bars
multiplier = 0

plt.figure(figsize=(20, 10))

x_axis = np.array(search_grid)

for i, row in enumerate(log_np):    
    offset = width * multiplier
    plt.bar(x_axis + offset, row, width, label=f"code{i}", log=True)
    
    #rects = ax.bar(x + offset, measurement, width, label=attribute)
    #ax.bar_label(rects, padding=3)
    multiplier += 1


plt.xticks(x_axis)
plt.xlabel("len(list)")
plt.ylabel("time")
plt.tight_layout()
plt.grid()
plt.legend(loc="best")
plt.show()