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What rand() does—and what it does not promise
In C++, rand() returns a pseudo-random integer from zero through RAND_MAX. The sequence is deterministic in the sense that it is generated by an algorithm, but the quality of that sequence is not guaranteed by the interface. Thread-safety is implementation-defined, so code should not assume concurrent calls behave safely on every implementation. cppreference’s C++ reference for rand() recommends the C++11 random facilities for serious random-number needs.
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These limitations do not prove that every use of rand() is slow, broken, or unsafe. A small, noncritical use may tolerate its constraints. The important question is whether its guarantees match the application—not whether the function is universally unusable.
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Why an embedded project stopped using rand()
Adam Dunkels described a specific problem in his team’s embedded build: the Newlib reentrancy layer allocated state through malloc() when rand() was first called, contributing to a memory and stack problem in their deployment. His account is an example of how the target library can affect a seemingly simple API, not evidence that all implementations allocate memory or cause the same failure. Dunkels’ 2022 account of the Newlib issue names PCG as one possible replacement and says of their response, “Fortunately, the solution is simple: we just stop using rand().”
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The lesson for embedded developers is to inspect the implementation and test the actual target build. Check whether initialization or calls allocate memory, what state is used, and how concurrent access is handled. A desktop result or a different C library cannot establish those properties for your firmware.
What to use instead in C++
C++11’s <random> separates pseudo-random engines from distributions. An engine generates a sequence; a distribution maps engine output to the range or statistical distribution the application needs. That separation makes the generator and the transformation explicit. cppreference’s C++ random library reference describes the facilities.
Choose an engine and distribution that suit the task rather than treating <random> as a single replacement function. If tests need repeatable results, use a fixed seed and keep the engine and distribution choices stable. If results must be unpredictable to an attacker, a deterministic pseudo-random engine is not sufficient merely because it comes from <random>; use a security-appropriate source for the platform and purpose.
What to use instead in C
C++’s <random> library is not available to a C program. Select an RNG implementation that works with the project’s language, target, memory budget, and quality requirements. PCG is one example mentioned in the embedded account, not a universal recommendation or proof that it fits every application.
Before adopting a C RNG, check its documentation and target implementation for seeding, output range, state size, concurrency behavior, reproducibility, and suitability for security-sensitive use. Verify memory and initialization behavior on the actual library and hardware. The evidence here does not establish one C RNG as best for all projects.
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| Requirement | What to establish |
|---|---|
| Statistical quality | Whether the generator’s documented properties are adequate for the application; the rand() interface does not guarantee sequence quality. |
| Reproducible tests | Whether the generator can be seeded and whether the same engine, seed, and mapping produce repeatable results in the environment you test. |
| Security-sensitive unpredictability | Whether the source is designed for the threat model. Do not treat ordinary deterministic pseudo-random generation as a cryptographic source. |
| Range or distribution | How generated values are mapped to the required range or distribution, and whether that mapping is part of the library or your own code. |
| Concurrency | What the implementation guarantees for concurrent calls and whether the chosen design needs separate state or synchronization. |
| Memory and target support | State size, initialization behavior, allocation, and availability in the exact library and target build. |
No broad controlled speed comparison is established by the cited material, so performance should be measured on the project’s actual target if it matters. Likewise, the embedded allocation incident is a reason to inspect a library, not a basis for assuming all rand() implementations behave alike.
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