[2026] C++ random numbers | Complete Guide to the C++11 random library

2026년 3월 12일 · 11분 읽기 · 수정 2026년 3월 12일 Beginner tutorial

이 글의 핵심

Why rand() is problematic, how to use random_device, mt19937, and distributions in C++11, with dice simulations, shuffling, weighted picks, seeding, and performance notes.

Problems with rand()

아래 코드는 cpp를 사용한 구현 예제입니다. 코드를 직접 실행해보면서 동작을 확인해보세요.

// ❌ Legacy style
srand(time(0));
int x = rand() % 100;  // 0-99
// Issues:
// 1. Not a uniform distribution
// 2. Low statistical quality
// 3. Not thread-safe

Modern random numbers (C++11)

다음은 cpp를 활용한 상세한 구현 코드입니다. 필요한 모듈을 import하고, 반복문으로 데이터를 처리합니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

#include <random>
int main() {
    // Seed
    random_device rd;
    
    // Engine
    mt19937 gen(rd());
    
    // Distribution
    uniform_int_distribution<> dis(1, 100);
    
    // Generate
    for (int i = 0; i < 10; i++) {
        cout << dis(gen) << " ";
    }
}

Random engines

아래 코드는 cpp를 사용한 구현 예제입니다. 코드를 직접 실행해보면서 동작을 확인해보세요.

// Mersenne Twister (recommended)
mt19937 gen32;      // 32-bit
mt19937_64 gen64;   // 64-bit
// Linear congruential (fast, lower quality)
minstd_rand gen;
// Subtract-with-carry
ranlux24 gen;

Distributions

Uniform

아래 코드는 cpp를 사용한 구현 예제입니다. 코드를 직접 실행해보면서 동작을 확인해보세요.

// Integers
uniform_int_distribution<> intDis(1, 6);  // dice
int dice = intDis(gen);
// Reals
uniform_real_distribution<> realDis(0.0, 1.0);
double x = realDis(gen);

Normal

normal_distribution<> normalDis(100.0, 15.0);  // mean 100, stddev 15
double iq = normalDis(gen);

Other distributions

다음은 cpp를 활용한 상세한 구현 코드입니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

// Bernoulli (true/false)
bernoulli_distribution coinFlip(0.5);  // 50%
bool result = coinFlip(gen);
// Binomial
binomial_distribution<> binDis(10, 0.5);
int heads = binDis(gen);
// Poisson
poisson_distribution<> poisDis(4.0);
int events = poisDis(gen);
// Exponential
exponential_distribution<> expDis(1.0);
double time = expDis(gen);

Practical examples

Example 1: Dice histogram

#include <random>
#include <map>
int main() {
    random_device rd;
    mt19937 gen(rd());
    uniform_int_distribution<> dice(1, 6);
    
    map<int, int> histogram;
    
    // Roll 10000 times
    for (int i = 0; i < 10000; i++) {
        int roll = dice(gen);
        histogram[roll]++;
    }
    
    // Print histogram
    for (const auto& [value, count] : histogram) {
        cout << value << ": " << string(count / 100, '*') << endl;
    }
}

Example 2: Random string

다음은 cpp를 활용한 상세한 구현 코드입니다. 반복문으로 데이터를 처리합니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

string generateRandomString(size_t length) {
    static random_device rd;
    static mt19937 gen(rd());
    static uniform_int_distribution<> dis(0, 61);
    
    const string chars = 
        "0123456789"
        "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
        "abcdefghijklmnopqrstuvwxyz";
    
    string result;
    for (size_t i = 0; i < length; i++) {
        result += chars[dis(gen)];
    }
    
    return result;
}
int main() {
    cout << generateRandomString(10) << endl;
    cout << generateRandomString(20) << endl;
}

Example 3: Shuffle

아래 코드는 cpp를 사용한 구현 예제입니다. 필요한 모듈을 import하고, 반복문으로 데이터를 처리합니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

#include <algorithm>
int main() {
    vector<int> v = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
    
    random_device rd;
    mt19937 gen(rd());
    
    shuffle(v.begin(), v.end(), gen);
    
    for (int x : v) {
        cout << x << " ";
    }
}

Example 4: Weighted discrete choice

#include <random>
int main() {
    random_device rd;
    mt19937 gen(rd());
    
    // Weights: 10%, 30%, 60%
    discrete_distribution<> dis({10, 30, 60});
    
    map<int, int> histogram;
    
    for (int i = 0; i < 10000; i++) {
        int choice = dis(gen);
        histogram[choice]++;
    }
    
    for (const auto& [choice, count] : histogram) {
        cout << "choice " << choice << ": " << count << " times" << endl;
    }
}

Seeding

아래 코드는 cpp를 사용한 구현 예제입니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

// Time-based (not reproducible)
mt19937 gen1(time(0));
// random_device (recommended for non-fixed seeds)
random_device rd;
mt19937 gen2(rd());
// Fixed seed (reproducible)
mt19937 gen3(12345);
// Seed sequence
seed_seq seq{1, 2, 3, 4, 5};
mt19937 gen4(seq);

Common pitfalls

Pitfall 1: Creating an engine every call

다음은 cpp를 활용한 상세한 구현 코드입니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

// ❌ Inefficient
int getRandom() {
    random_device rd;
    mt19937 gen(rd());  // created every time (slow)
    uniform_int_distribution<> dis(1, 100);
    return dis(gen);
}
// ✅ static storage
int getRandom() {
    static random_device rd;
    static mt19937 gen(rd());
    static uniform_int_distribution<> dis(1, 100);
    return dis(gen);
}

Pitfall 2: Reseeding inside a loop

아래 코드는 cpp를 사용한 구현 예제입니다. 반복문으로 데이터를 처리합니다. 각 부분의 역할을 이해하면서 코드를 살펴보시기 바랍니다.

// ❌ Same short sequence each iteration
for (int i = 0; i < 10; i++) {
    mt19937 gen(12345);  // always the same seed
    cout << gen() << endl;
}
// ✅ Reuse one engine
mt19937 gen(12345);
for (int i = 0; i < 10; i++) {
    cout << gen() << endl;
}

Pitfall 3: Biased ranges with rand()

아래 코드는 cpp를 사용한 구현 예제입니다. 코드를 직접 실행해보면서 동작을 확인해보세요.

// ❌ Biased
int x = rand() % 100;  // not uniform
// ✅ Uniform distribution
uniform_int_distribution<> dis(0, 99);
int x = dis(gen);

Performance comparison

#include <chrono>
int main() {
    const int N = 10000000;
    
    // rand()
    srand(time(0));
    auto start = chrono::high_resolution_clock::now();
    for (int i = 0; i < N; i++) {
        int x = rand();
    }
    auto end = chrono::high_resolution_clock::now();
    cout << "rand(): " << chrono::duration_cast<chrono::milliseconds>(end - start).count() << "ms" << endl;
    
    // mt19937
    random_device rd;
    mt19937 gen(rd());
    start = chrono::high_resolution_clock::now();
    for (int i = 0; i < N; i++) {
        int x = gen();
    }
    end = chrono::high_resolution_clock::now();
    cout << "mt19937: " << chrono::duration_cast<chrono::milliseconds>(end - start).count() << "ms" << endl;
}

FAQ

Q1: rand() vs the random library?

rand(): legacy, poor quality.
random: modern, higher quality, more flexible.

Q2: Must I always use random_device?

A: Use it for seeding. Generate numbers with an engine.

Q3: Which engine should I use?

A: In most cases mt19937 is a solid default.

Q4: How do I get reproducible sequences?

A: Use a fixed seed.

Q5: Is it thread-safe?

A: Give each thread its own engine (and distribution if needed).

Q6: Learning resources?

cppreference.com
The C++ Standard Library (Nicolai Josuttis)
Effective Modern C++

Practical tips

Debugging

Fix compiler warnings first.
Reproduce with a small test case.

Performance

Do not optimize without profiling.
Define measurable goals first.

Code review

Catch common review issues early.
Follow team conventions.

Production checklist

Before coding

Is this the right approach?
Can teammates maintain it?
Does it meet performance needs?

While coding

Warnings cleared?
Edge cases covered?
Error handling adequate?

At review

Intent clear?
Tests sufficient?
Documentation where needed?

[2026] C++ random numbers | Complete Guide to the C++11 random library

이 글의 핵심

Problems with rand()

Modern random numbers (C++11)

Random engines

Distributions

Uniform

Normal

Other distributions

Practical examples

Example 1: Dice histogram

Example 2: Random string

Example 3: Shuffle

Example 4: Weighted discrete choice

Seeding

Common pitfalls

Pitfall 1: Creating an engine every call

Pitfall 2: Reseeding inside a loop

Pitfall 3: Biased ranges with rand()

Performance comparison

FAQ

Q1: rand() vs the random library?

Q2: Must I always use random_device?

Q3: Which engine should I use?

Q4: How do I get reproducible sequences?

Q5: Is it thread-safe?

Q6: Learning resources?

Practical tips

Debugging

Performance

Code review

Production checklist

Before coding

While coding

At review

Keywords

C++, random, C++11, mt19937, uniform distribution

이 글의 핵심

Problems with rand()

Modern random numbers (C++11)

Random engines

Distributions

Uniform

Normal

Other distributions

Practical examples

Example 1: Dice histogram

Example 2: Random string

Example 3: Shuffle

Example 4: Weighted discrete choice

Seeding

Common pitfalls

Pitfall 1: Creating an engine every call

Pitfall 2: Reseeding inside a loop

Pitfall 3: Biased ranges with rand()

Performance comparison

FAQ

Q1: rand() vs the random library?

Q2: Must I always use random_device?

Q3: Which engine should I use?

Q4: How do I get reproducible sequences?

Q5: Is it thread-safe?

Q6: Learning resources?

Related posts (internal)

Practical tips

Debugging

Performance

Code review

Production checklist

Before coding

While coding

At review

Keywords

C++, random, C++11, mt19937, uniform distribution

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