Maximizing Efficiency of Collatz Conjecture Program Python-CodePudding

My question is very simple.

I wrote this program for pure entertainment. It takes a numerical input and finds the length of every Collatz Sequence up to and including that number.

I want to make it faster algorithmically or mathematically (i.e. I know I could make it faster by running multiple versions parallel or by writing it in C , but where's the fun in that?).

Any and all help is welcome, thanks!

EDIT: Code further optimized with the help of dankal444

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

# Get Range to Check
top_range = int(input('Top Range: '))

@nb.njit('int64[:](int_)')
def collatz(top_range):
    # Initialize mem
    mem = np.zeros(top_range   1, dtype = np.int64)
    for start in range(2, top_range   1):
        # If mod4 == 1: (3x   1)/4
        if start % 4 == 1:
            mem[start] = mem[(start   (start >> 1)   1) // 2]   3
        
        # If 4mod == 3: 3(3x   1)   1 and continue
        elif start % 4 == 3:
            num = start   (start >> 1)   1
            num  = (num >> 1)   1
            count = 4

            while num >= start:
                if num % 2:
                    num  = (num >> 1)   1
                    count  = 2
                else:
                    num //= 2
                    count  = 1
            mem[start] = mem[num]   count

        # If 4mod == 2 or 0: x/2
        else:
            mem[start] = mem[(start // 2)]   1

    return mem

mem = collatz(top_range)

# Plot each starting number with the length of it's sequence
plt.scatter([*range(1, len(mem)   1)], mem, color = 'black', s = 1)
plt.show()

CodePudding user response：

Applying numba on your code does help by much.

I removed tqdm since it does not help with performance.

import time
from matplotlib import pyplot as plt
from tqdm import tqdm

import numpy as np
import numba as nb
@nb.njit('int64[:](int_)')
def collatz2(top_range):
    mem = np.zeros(top_range   1, dtype=np.int64)
    for start in range(2, top_range   1):
        # If mod(4) == 1: Value 2 or 3 Cached
        if start % 4 == 1:
            mem[start] = mem[(start   (start >> 1)   1) // 2]   3
        # If mod(4) == 3: Use Algorithm
        elif start % 4 == 3:
            num = start
            count = 0
            while num >= start:
                if num % 2:
                    num  = (num >> 1)   1
                    count  = 2
                else:
                    num //= 2
                    count  = 1
            mem[start] = mem[num]   count
        # If mod(4) == 2 or 4: Value 1 Cached
        else:
            mem[start] = mem[(start // 2)]   1
    return mem


def collatz(top_range):
    mem = [0] * (top_range   1)
    for start in range(2, top_range   1):
        # If mod(4) == 1: Value 2 or 3 Cached
        if start % 4 == 1:
            mem[start] = mem[(start   (start >> 1)   1) // 2]   3
        # If mod(4) == 3: Use Algorithm
        elif start % 4 == 3:
            num = start
            count = 0
            while num >= start:
                if num % 2:
                    num  = (num >> 1)   1
                    count  = 2
                else:
                    num //= 2
                    count  = 1
            mem[start] = mem[num]   count
        # If mod(4) == 2 or 4: Value 1 Cached
        else:
            mem[start] = mem[(start // 2)]   1
    return mem

# profiling here
def main():

    top_range = 1_000_000
    mem = collatz(top_range)
    mem2 = collatz2(top_range)
    assert np.allclose(np.array(mem), mem2)

For top_range = 1_000 optimized function is ~100x faster. For top_range = 1_000_000, the optimized function is about 600x faster:

    79                                           def main():
    81         1          3.0      3.0      0.0      top_range = 1_000_000
    83         1   24633045.0 24633045.0     98.7      mem = collatz(top_range)
    85         1      39311.0  39311.0      0.2      mem2 = collatz2(top_range)