When Should You Choose the New Random Library Over std::rand()?

Why is the new random library better than std::rand()?

While the new random library offers superior quality and flexibility, it may not always be the best choice in every situation. Here's why:

Pros of New Random Library:

• Specified Implementation: Algorithms are fully specified, ensuring consistent behavior across compilers.
• Guaranteed Quality: Generators meet high performance standards, ensuring reliable randomness.
• Encapsulated Classes: No global state, preventing threading and locality issues.
• Random Device: Default random_device for cross-platform seeding.

Cons of New Random Library:

• Speed: Typically slower than std::rand(), especially for simpler generators like Mersenne Twister.
• Complexity: More complex to use, especially when needing reproducible sequences within specific modules.

Comparison with Specific Example:

In your experiment, std::rand() and std::mt19937 std::uniform_int_distribution gave comparable randomization quality. However, the new library was significantly slower due to the use of Mersenne Twister, a slower but higher-quality generator.

Potential Improvements:

• Use std::minstd_rand instead of std::mt19937 for faster performance without compromising quality.
• Avoid static variables and inline functions for further speed improvements.
• Consider implementing your own fast and high-quality generator like XorShift64*.

Conclusion:

While the new random library offers advancements in quality and flexibility, std::rand() remains a viable option for simple cases where speed is crucial. However, for reproducible simulations, cross-platform compatibility, or higher randomness requirements, the new library is recommended.

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