There is nothing wrong with rand per se, but there is a lot of confusion about what rand does and how it does it. The often results in rand being used in situations where it is a very poor choice. In general, rand should be avoided except in the most simple of cases where pseudorandom numbers are desired, and even then there are caveats. So the question is, what exactly is the problem with rand? Let us count the ways:

  • Rangerand returns numbers in the range of [0, RAND_MAX), and RAND_MAX is specified with a minimum value of 32,767. Portable code cannot assume that the range is any larger than this, so portable code is very limited in the random numbers that can be predictably generated. Compare this with better random number generators that have a range of 32 or 64 bits.
  • Period: The period of rand is implementation defined, but typically falls around 2^32-1. Compare this with the Mersenne Twister algorithm, which is 2^19937-1. You typically want the period of a random number generator to exceed the amount of numbers expected to be generated, because that’s the point where the sequence repeats.
  • Qualityrand has a history of poor implementations, where the quality of the numbers generated were either very predictable or not ‘random’ enough. Notable in these implementations the low order bits were decidedly not random, which prompted programmers to only use the high order bits for adjusting ranges (which we’ll see shortly).
  • Distribution: First and foremost, rand is not required to conform to any standard distribution. Typically, the base random number generator is expected to return values in a uniform distribution. That is, every number in the range is equally likely to be returned for any given request. Even when rand claims a uniform distribution, it may not be a terribly good distribution, which greatly affects any attempts to adjust the range.

So when should you use rand? According to the above, rand is fine to use when you don’t need numbers outside of [0, 32,767), won’t generate more numbers than the common period, don’t particularly care about the quality of the pseudorandom sequence, and don’t need the sequence to conform to a strict distribution. That’s a lot of not caring for my tastes, but there are certainly situations where all of them apply.

However, there is another wrench that can be thrown in our gears, because a great many applications need a smaller range than [0, 32,767). For this, the programmer typically adjusts the range by forcing a number returned by rand into a smaller range. For example, to roll a 6 sided die, one would prefer to only receive values in the range of [0, 6). This is often done using the remainder operator:

int r = rand() % N;

Or if both a lower and upper bound are needed:

int r = LO + rand() % (HI - LO);

Anyone who does this will be rewarded with a seemingly random sequence and be thrilled that their clever solution worked. Unfortunately, this does not work. The first solution only works when N evenly divides into RAND_MAX. The second solution isn’t any better. The reason is because forcing the range in this way eliminates any chance of having a uniform distribution. Now, this is okay if you care nothing about some numbers being more probable than others, but to be correct, you must work with the distribution instead of destroy it.

Why does the distribution get destroyed though? It’s the pigeonhole principle. If you have a bunch of numbers and try to merge them into a smaller number of buckets, unless the number of buckets is evenly divisible by the range of numbers, some buckets will be given more numbers than other buckets. Thus the distribution of numbers in the buckets is worse than the distribution of numbers in the original range.

Most experienced C programmers will tell you that using remainder with random numbers is bad, but not many of them will be able to tell you why. This is similar to the goto issue, where beginners are taught to blindly believe that any code with a goto statement is spaghetti code and any use of goto is evil. The solution suggested to get around the remainder issue is usually division by RAND_MAX. For a range of [0, N), you would do something like this:

int r = rand() / (RAND_MAX / N + 1);

With the lower and upper bound support looking like this:

int r = M + rand() / (RAND_MAX / (N - M) + 1);

All is well, right? This produces a seemingly random sequence. The seasoned community is happy because you are not using the remainder operator with rand, and you are happy because you got your random numbers and foiled the evils of rand. But wait! This is not an improvement…at all. This ‘solution’ does nothing to alleviate distribution problems. In reality, it is a fix for poor implementations of rand where the low order bits are very non-random. The remainder operator uses the low order bits primarily while the ‘fix’ uses the high order bits. It may help, but it doesn’t do jack diddly where distribution is concerned.

real fix is to work with the natural distribution rather than try to circumvent it. Simply call rand until you get a number that fits within your selected range:

int r;

    r = rand();
} while (r >= HI);

Or for both lower and upper bounds:

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int r;

    r = rand();
} while (r < LO || r >= HI);

Not only does this fix the distribution problem, it also avoids the low order bit problem entirely. The down side to this solution is that the performance is dictated by the range. A small range could potentially call rand many times. Unfortunately, there is not a simple workaround for this if you are interested in maintaining the natural distribution of the random number generator. Further, this does not fix any problems in the natural distrubtion of the random number generator. If rand does not have a good uniform distribution naturally, the above loops will not improve matters.

Often you’ll see recommendations to introduce floating-point to ‘fix’…something. I myself made this recommendation in a previous incarnation of this article, and it shames me to admit it. The problem is floating-point creates its own problems, and those problems often exacerbate the problems you’re using it to solve. As they say, if you have 1 problem and decide to use floating-point, now you have 2.000001 problems. Please do not use floating-point to try fixing distribution issues with rand, it will only result in further heartache.

It should be kept in mind that these distribution problems are consistent with all random number generators that return a ranged integer, not just rand. So please do not read this article as me harping on rand, rather read it as me harping on the majority of programmers who use it incorrectly. I suppose that’s not much better, but it is what it is. 🙂 And note that even if you are using the most awesome random number generator available, adjusting the natural range will require some thought if it needs to be done correctly.

Seeding rand

Probably the most vexing problem with how people use rand is how the generator is seeded. The usual solution is to get the system time using the time function. In theory, this is a great idea because not only does it seed the random number generator with an ever changing value, but it is also portable:

srand((unsigned int)time(NULL));

Right? Well no, not really. The issue is that time_t is a restricted type, and may not be meaningfully converted to unsigned int. That is not a terribly big issue as I have yet to see a system where it would fail to work correctly, and I do not know anyone who has either. But subtle lingering portability issues should leave a sour taste in the mouth. Fortunately, there is a way to use the result of time portably as a seed for rand; just hash the bytes of a time_t:

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unsigned time_seed()
    time_t now = time(0);
    unsigned char *p = (unsigned char *)&now;
    unsigned seed = 0;

    for (size_t i = 0; i < sizeof now; i++)
        seed = seed * (UCHAR_MAX + 2U) + p[i];

    return seed;


srand (time_seed());

A hash will take advantage of the way the system time changes. Even better, the C and C++ standards guarantee that type punning is a portable operation for simple types, and time_t is a simple type. So hashing the system time and seeding rand with it is a portable solution with desirable properties.

Of course, one might wonder why we should go to all of the trouble when time_t is extremely likely to work. The uncertainty of being able to use time_t alone should be a good enough reason to avoid directly seeding rand with one.

Now for the caveat, and there always seems to be one. 🙁 The resolution for time is 1 second. That’s not a requirement, but it is the overwhelming choice in implementations. This means that calling time_seed multiple times in a second will result in the same seed. Further, because the time is predictable, the seed will also be predictable. Unfortunately, I have yet to find a better way to seed a random number generator in C in a portable manner. I’ve actually received a lot of feedback on this (thank you! keep it coming), but none of them were portable. The best one was a hash of the environ variable in POSIX-based systems, and while it was very interesting, for such systems I would prefer simply using /dev/random from the beginning to avoid all of the problems from this article entirely.

Honorable Mention (C++11)

The C++11 standard introduced the <random> header, which offers some amazingly effective, yet equally amazingly confusing random number utilities. Ultimately for the average programmer it is rather simple, but you need to recognize that the majority of the classes can be ignored. Typically, the only ones that matter are:

  • std::mt19337: Mersenne Twister 32 bit implementation.
  • std::mt19337_64: Mersenne Twister 64 bit implementation.
  • std::random_device: Implementation-dependent random number generator.
  • std::uniform_int_distribution: Accurately adjusts numbers from a generator into a uniform integer range.
  • std::uniform_real_distribution: Accurately adjusts numbers from a generator into a uniform floating-point range.

Using the library is straightforward. You create a random number generator (an engine object), create a distribution object, then apply the engine object to the distribution object to get the random numbers you want using operator() of the distribution object. Since std::random_device is notably slower than the Mersenne Twister, my preference is to use it only for seeding the Mersenne Twister algorithm in cases where Mersenne Twister is the primary engine:

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#include <iostream>
#include <random>

int main()
    std::random_device seed;
    std::mt19937 gen(seed());
    std::uniform_int_distribution<int> dist(0, 100);

    for (int i = 0; i < 10; i++)
        std::cout << dist(gen) << '\n';

Obviously, if you’re writing code in C, this option is not available. But for C++ programmers with access to a C++11 compiler, you should probably forget that rand exists and use the new <random> library. If you don’t have access to a C++11 compiler, the Boost library is the order of the day to get a decent facsimile of the standard <random> library. Boost is awesome in general, and I’d recommend checking it out as it contains a lot of libraries and acts as a test bed for additions to the standard.


There is technically really nothing wrong with rand, despite what you may be told. The problem is with the people who use it, and how they use it incorrectly or with unreasonable expectations. Another problem is that one can use rand incorrectly and still get seemingly random results, so the incorrect use appears to work as expected.

Now for the big question: Does any of this matter to you? Yes, of course it does. A keen understanding of how things work will give you insight into writing good code. Will you encounter these problems in everyday programming? Probably not. Pseudorandom numbers are typically expected to be sufficiently random for the application but no more. As such, most people who use rand will either not see, or not care, about the discussed issues. In fact, these issues are generally not noticeable except on a very large scale or a very small scale.

An article on rand isn’t complete without the usual caveat that rand isn’t required to be a strong random number generator. In fact, it’s usually pretty weak. For little things that don’t need a good random number generator, rand will do fine, and whatever option you use for adjusting ranges will be equally fine. For everything else, a much stronger algorithm with more guarantees, such as the Mersenne Twister, is strongly recomme

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