这是我一直在玩的埃拉托色尼筛子的Python实现。

def eratosthenes(maximum: int) -> list[int | None]:
    """
    Find all the prime numbers between 2 and `maximum`.

    Args:
        maximum: The maximum number to check.

    Returns:
        A list of primes between 2 and `maximum`.
    """

    if maximum < 2:
        return []

    # Discard even numbers by default.
    sequence = dict.fromkeys(range(3, maximum+1, 2), True)

    for num, is_prime in sequence.items():
        # Already filtered, let's skip it.
        if not is_prime:
            continue

        # Avoid marking the same number twice.
        for num2 in range(num ** 2, maximum+1, num):
            # Here, `num2` might contain an even number - skip it.
            if num2 in sequence:
                sequence[num2] = False

    # Re-add 2 as prime and filter out the composite numbers.
    return [2] + [num for num, is_prime in sequence.items() if is_prime]

在一台简陋的三星Galaxy A40上，该代码大约需要16秒才能输入10000000个数字。

欢迎提出建议!

这取决于您的应用程序。这里有一些注意事项:

你需要的仅仅是一些数字是否是质数的信息，你需要所有的质数达到一定的限度，还是你需要(潜在的)所有的质数? 你要处理的数字有多大?

米勒-拉宾和模拟测试只比筛选超过一定规模的数字(我相信大约在几百万左右)的速度快。在这以下，使用试除法(如果你只有几个数字)或筛子会更快。

我最近写了这段代码来求数字的和。它可以很容易地修改，以确定一个数字是否是质数。基准测试在代码之上。

// built on core-i2 e8400
// Benchmark from PowerShell
// Measure-Command { ExeName.exe }
// Days              : 0
// Hours             : 0
// Minutes           : 0
// Seconds           : 23
// Milliseconds      : 516
// Ticks             : 235162598
// TotalDays         : 0.00027217893287037
// TotalHours        : 0.00653229438888889
// TotalMinutes      : 0.391937663333333
// TotalSeconds      : 23.5162598
// TotalMilliseconds : 23516.2598
// built with latest MSVC
// cl /EHsc /std:c++latest main.cpp /O2 /fp:fast /Qpar

#include <cmath>
#include <iostream>
#include <vector>

inline auto prime = [](std::uint64_t I, std::vector<std::uint64_t> &cache) -> std::uint64_t {
    std::uint64_t root{static_cast<std::uint64_t>(std::sqrtl(I))};
    for (std::size_t i{}; cache[i] <= root; ++i)
        if (I % cache[i] == 0)
            return 0;

    cache.push_back(I);
    return I;
};

inline auto prime_sum = [](std::uint64_t S) -> std::uint64_t {
    std::uint64_t R{5};
    std::vector<std::uint64_t> cache;
    cache.reserve(S / 16);
    cache.push_back(3);

    for (std::uint64_t I{5}; I <= S; I += 8)
    {
        std::uint64_t U{I % 3};
        if (U != 0)
            R += prime(I, cache);
        if (U != 1)
            R += prime(I + 2, cache);
        if (U != 2)
            R += prime(I + 4, cache);
        R += prime(I + 6, cache);
    }
    return R;
};

int main()
{
    std::cout << prime_sum(63210123);
}

另一个Python实现比死亡面具推销员的答案更直接，也更快:

import numpy as np

def prime_numbers(limit: int) -> list[int]:
    """Provide a list of all prime numbers <= the limit."""
    is_prime = np.full((limit + 1, ), True)
    is_prime[0:2] = False
    for n in range(2, limit + 1):
        if is_prime[n]:
            is_prime[n**2::n] = False
    return list(np.where(is_prime)[0])

你可以进一步优化，例如，排除2，或者硬编码更多质数，但我想保持简单。

*示例运行时比较(注意:我使用了其他实现的优化形式，见我的评论):

这是我一直在玩的埃拉托色尼筛子的Python实现。

def eratosthenes(maximum: int) -> list[int | None]:
    """
    Find all the prime numbers between 2 and `maximum`.

    Args:
        maximum: The maximum number to check.

    Returns:
        A list of primes between 2 and `maximum`.
    """

    if maximum < 2:
        return []

    # Discard even numbers by default.
    sequence = dict.fromkeys(range(3, maximum+1, 2), True)

    for num, is_prime in sequence.items():
        # Already filtered, let's skip it.
        if not is_prime:
            continue

        # Avoid marking the same number twice.
        for num2 in range(num ** 2, maximum+1, num):
            # Here, `num2` might contain an even number - skip it.
            if num2 in sequence:
                sequence[num2] = False

    # Re-add 2 as prime and filter out the composite numbers.
    return [2] + [num for num, is_prime in sequence.items() if is_prime]

在一台简陋的三星Galaxy A40上，该代码大约需要16秒才能输入10000000个数字。

欢迎提出建议!

Rabin-Miller是一个标准的概率质数检验。(你运行K次，输入数字要么肯定是合数，要么可能是素数，误差概率为4-K。(经过几百次迭代，它几乎肯定会告诉你真相)

拉宾·米勒有一个非概率(确定性)的变体。

The Great Internet Mersenne Prime Search (GIMPS) which has found the world's record for largest proven prime (274,207,281 - 1 as of June 2017), uses several algorithms, but these are primes in special forms. However the GIMPS page above does include some general deterministic primality tests. They appear to indicate that which algorithm is "fastest" depends upon the size of the number to be tested. If your number fits in 64 bits then you probably shouldn't use a method intended to work on primes of several million digits.