Alternative to rand() for avoid race conditions? - c++

According to : http://www.cplusplus.com/reference/cstdlib/rand/
In C, the generation algorithm used by rand is guaranteed to only be
advanced by calls to this function. In C++, this constraint is
relaxed, and a library implementation is allowed to advance the
generator on other circumstances (such as calls to elements of
).
But then over here it says :
The function accesses and modifies internal state objects, which may
cause data races with concurrent calls to rand or srand.
Some libraries provide an alternative function that explicitly avoids
this kind of data race: rand_r (non-portable).
C++ library implementations are allowed to guarantee no data races for
calling this function.
Ideally I would like to have some kind of "instance" of rand, so that for that instance, and a given seed, I always generate the same sequence of numbers for calls to THAT instance . With the current versions it seems that in some platforms, calls by other functions to rand() (perhaps even on different threads), could affect the sequence of numbers generated in my thread by my code.
Is there an alternative, where I can hold on to some kind of "instance", where I am guaranteed to generate a particular sequence, given a seed, and where other calls to different "instances" do not affect it ?
EDIT: For clarity - my code is going to run on multiple different platforms (iOS, Android, Windows 8.1, Windows 10, Linux etc), and it isn't possible for me to test every implementation at present. I would just like to implement things based on what is guaranteed by the standard...

You can make use of std::uniform_int_distribution and std::mt19937 to keep a generator with your common seed (all from <random> library).
std::mt19937 gen(SEED);
std::uniform_int_distribution<> dis(MIN, MAX);
auto random_number = dis(gen);
Here, SEED is the seed number you want to specify. You can set another seed later with the .seed method too:
std::mt19937 gen{};
gen.seed(SEED);
If you need to generate one, you can use std::random_device for that:
std::random_device rd{};
std::mt19937 gen(rd());
The dis(MIN, MAX) part sets a range of min and max values this distribution can come up with, which means it will never generate a value bigger than MAX, or smaller than MIN.
Finally, you can use your generator with this distribution to generate your wanted random values like so: dis(gen). The distribution can take any generator, so if you want other distributions with the same sequence of random numbers, you may make a copy of gen, or use the same seed and construct two or more generators.

use random() instead of rand().
https://www.securecoding.cert.org/confluence/display/c/MSC30-C.+Do+not+use+the+rand%28%29+function+for+generating+pseudorandom+numbers
https://www.securecoding.cert.org/confluence/display/c/CON33-C.+Avoid+race+conditions+when+using+library+functions

Related

std::uniform_real_distribution and rand()

Why is std::uniform_real_distribution better than rand() as the random number generator? Can someone give an example please?
First, it should be made clear that the proposed comparison is nonsensical.
uniform_real_distribution is not a random number generator. You cannot produce random numbers from a uniform_real_distribution without having a random number generator that you pass to its operator(). uniform_real_distribution "shapes" the output of that random number generator into an uniform real distribution. You can plug various kinds of random number generators into a distribution.
I don't think this makes for a decent comparison, so I will be comparing the use of uniform_real_distribution with a C++11 random number generator against rand() instead.
Another obvious difference that makes the comparison even less useful is the fact that uniform_real_distribution is used to produce floating point numbers, while rand() produces integers.
That said, there are several reasons to prefer the new facilities.
rand() is global state, while when using the facilities from <random> there is no global state involved: you can have as many generators and distributions as you want and they are all independent from each other.
rand() has no specification about the quality of the sequence generated. The random number generators from C++11 are all well-specified, and so are the distributions. rand() implementations can be, and in practice have been, of very poor quality, and not very uniform.
rand() provides a random number within a predefined range. It is up to the programmer to adjust that range to the desired range. This is not a simple task. No, it is not enough to use % something. Doing this kind of adjustment in such a naive manner will most likely destroy whatever uniformity was there in the original sequence. uniform_real_distribution does this range adjustment for you, correctly.
The real comparison is between rand and one of the random number engines provided by the C++11 standard library. std::uniform_real_distribution just distributes the output of an engine according to some parameters (for example, real values between 10 and 20). You could just as well make an engine that uses rand behind the scenes.
Now the difference between the standard library random number engines and using plain old rand is in guarantee and flexibility. rand provides no guarantee for the quality of the random numbers - in fact, many implementations have shortcomings in their distribution and period. If you want some high quality random numbers, rand just won't do. However, the quality of the random number engines is defined by their algorithms. When you use std::mt19937, you know exactly what you're getting from this thoroughly tested and analysed algorithm. Different engines have different qualities that you may prefer (space efficiency, time efficiency, etc.) and are all configurable.
This is not to say you should use rand when you don't care too much. You might as well just start using the random number generation facilities from C++11 right away. There's no downside.
The reason is actually in the name of the function, which is the fact that the uniformity of the distribution of random numbers is better with std::uniform_real_distribution compared to the uniform distribution of random numbers that rand() provides.
The distribution for std::uniform_real_distribution is of course between a given interval [a,b).
Essentially, that is saying that the probability density that when you ask for a random number between 1 and 10 is as great of getting 5 or getting 9 or any other of the possible values with std::uniform_real_distribution, as when you'd do it with rand() and call it several times, the probability of getting 5 instead of 9 may be different.

Correctly seeding random number generator (Mersenne twister) c++

Besides being a rubbish programmer, my jargon is not up to scratch. I am going to try my best to explain myself.
I have implemented a Merssene twister random number generator using randomlib.
Admittedly I am not too familiar on how Visual 8 C++'s random number generator works, but I find I can seed it once srand(time(NULL)) in main() and I can safely use rand() in my other classes.
The Merssene twister that I have one needs to create an object, and then seed that object.
#include <RandomLib/Random.hpp>
RandomLib::Random r; // create random number object
r.Reseed(); // seed with a "unique" seed
float d = r.FloatN(); // a random in [0,1] rounded to the nearest double
If I want to generate a random number in a class how do I do this without having to define an object each time. I am just worried that if I use the computer clock I will use the same seed each run (only changes every second).
Am I explaining myself right?
Thanks in advance
The Random object is essentially state information that you need to preserve. You can use all the normal techniques: You could have it as a global variable or pass it around as a parameter. If a particular class needs random numbers you can keep a Random object as a class member to provide randomness for that class.
The C++ <random> library is similar in that it requires the construction of an object as the source of randomness/RNG state. This is a good design because it allows the program to control access to the state and, for example, guarantee good behavior with multiple threads. The C++ <random> library even includes mersenne twister algorithm.
Here's an example showing saving a RNG state as a class member (using std::mt19937 instead of Random)
#include <random> // for mt19937
#include <algorithm> // for std::shuffle
#include <vector>
struct Deck {
std::vector<Cards> m_cards;
std::mt19937 eng; // save RNG state as class member so we don't have to keep creating one
void shuffle() {
std::shuffle(std::begin(m_cards), std::end(m_cards), eng);
}
};
int main() {
Deck d;
d.shuffle();
d.shuffle(); // this reuses the RNG state as it was at the end of the first shuffle, no reseeding
}
The accepted answer does not actually seed its mt19937, see this Q&A for a more thorough and complete answer on how this might be achieved and why there is no single solution:
How to succinctly, portably, and thoroughly seed the mt19937 PRNG?
TL;DR:
The question is relating to RandomLib but I will answer by referring to the STL implementations due to <random> being more accessible 10 years on. The principles should apply to all mt19937 implementations however.
std::mt19937 and std::mt19937_64 have an internal default seed which provides some state for the engine to work off. The default seeds will cause the engine to produce the same values every time unless re-seeded.
std::mt19937 provides two methods to seed it, both via the seed() function.
The first overload accepts a param of result_type (uint32_t for std::mt19937 and uint64_t for std::mt19937_64). Internally (at least in the MSVC implementation) this function will use the provided seed value to fill its internal state through a series of bit fiddling ops. Most quick-and-dirty examples will use a std::random_device to provide this seed value, but due to the standard allowing random_device to be just another PRNG it cannot be relied on in all circumstances, apparently this is (or was) the case with the MinGW compiler on Windows.
The second overload accepts a more generic generator/range param which can be used with std::seed_seq. The linked question has an example of how to create one of these.
Creating a seed_seq or a sufficiently random initial seed is a challenge and why the linked question is provided.
It is not recommended that you create a new Mersenne Twister PRNG every time you need one due to the seeding process being non-trivial. Instead, it is better to declare one once and hold onto it, either as a static, thread_local, global, or member of a class with a long lifetime.

Random Number Generator: Should it be used as a singleton?

I use random numbers in several places and usually construct a random number generator whenever I need it. Currently I use the Marsaglia Xorshift algorithm seeding it with the current system time.
Now I have some doubts about this strategy:
If I use several generators the independence (randomness) of the numbers between the generators depends on the seed (same seed same number). Since I use the time (ns) as seed and since this time changes this works but I am wondering whether it would not be better to use only one singular generator and e.g. to make it available as a singleton. Would this increase the random number quality ?
Edit: Unfortunately c++11 is not an option yet
Edit: To be more specific: I am not suggesting that the singleton could increase the random number quality but the fact that only one generator is used and seeded. Otherwise I have to be sure that the seeds of the different generators are independent (random) from another.
Extreme example: I seed two generators with exactly the same number -> no randomness between them
Suppose you have several variables, each of which needs to be random, independent from the others, and will be regularly reassigned with a new random value from some random generator. This happens quite often with Monte Carlo analysis, and games (although the rigor for games is much less than it is for Monte Carlo). If a perfect random number generator existed, it would be fine to use a single instantiation of it. Assign the nth pseudo random number from the generator to variable x1, the next random number to variable x2, the next to x3, and so on, eventually coming back to variable x1 on the next cycle. around. There's a problem here: Far too many PRNGs fail the independence test fail the independence test when used this way, some even fail randomness tests on individual sequences.
My approach is to use a single PRNG generator as a seed generator for a set of N instances of self-contained PRNGs. Each instance of these latter PRNGs feeds a single variable. By self-contained, I mean that the PRNG is an object, with state maintained in instance members rather than in static members or global variables. The seed generator doesn't even need to be from the same family as those other N PRNGs. It just needs to be reentrant in the case that multiple threads are simultaneously trying to use the seed generator. However, In my uses I find that it is best to set up the PRNGs before threading starts so as to guarantee repeatability. That's one run, one execution. Monte Carlo techniques typically need thousands of executions, maybe more, maybe a lot more. With Monte Carlo, repeatability is essential. So yet another a random seed generator is needed. This one seeds the seed generator used to generate the N generators for the variables.
Repeatability is important, at least in the Monte Carlo world. Suppose run number 10234 of a long Monte Carlo simulation results in some massive failure. It would be nice to see what in the world happened. It might have been a statistical fluke, it might have been a problem. The problem is that in a typical MC setup, only the bare minimum of data are recorded, just enough for computing statistics. To see what happened in run number 10234, one needs to repeat that particular case but now record everything.
You should use the same instance of your random generator class whenever the clients are interrelated and the code needs "independent" random number.
You can use different objects of your random generator class when the clients do not depend on each other and it does not matter whether they receive the same numbers or not.
Note that for testing and debugging it is very useful to be able to create the same sequence of random numbers again. Therefore you should not "randomly seed" too much.
I don't think that its increasing the randomness but it reduces the memory you need to create an object every time you want to use the random generator. If this generator doesn't have any instance specific settings you can make a singleton.
Since I use the time (ns) as seed and since this time changes this works but I am wondering whether it would not be better to use only one singular generator and e.g. to make it available as a singleton.
This is a good example when the singleton is not an anti-pattern. You could also use some kind of inversion of control.
Would this increase the random number quality ?
No. The quality depends on the algorithm that generate random numbers. How you use it is irrelevant (assuming it is used correctly).
To your edit : you could create some kind of container that holds objects of your RNG classes (or use existing containers). Something like this :
std::vector< Rng > & RngSingleton()
{
static std::vector< Rng > allRngs( 2 );
return allRngs;
}
struct Rng
{
void SetSeed( const int seen );
int GenerateNumber() const;
//...
};
// ...
RngSingleton().at(0).SetSeed( 55 );
RngSingleton().at(1).SetSeed( 55 );
//...
const auto value1 = RngSingleton().at(0).GenerateNumber;
const auto value2 = RngSingleton().at(1).GenerateNumber;
Factory pattern to the rescue.
A client should never have to worry about the instantiation rules of its dependencies.
It allows for swapping creation methods. And the other way around, if you decide to use a different algorithm you can swap the generator class and the clients need no refactoring.
http://www.oodesign.com/factory-pattern.html
--EDIT
Added pseudocode (sorry, it's not c++, it's waaaaaay too long ago since I last worked in it)
interface PRNG{
function generateRandomNumber():Number;
}
interface Seeder{
function getSeed() : Number;
}
interface PRNGFactory{
function createPRNG():PRNG;
}
class MarsagliaPRNG implements PRNG{
constructor( seed : Number ){
//store seed
}
function generateRandomNumber() : Number{
//do your magic
}
}
class SingletonMarsagliaPRNGFactory implements PRNGFactory{
var seeder : Seeder;
static var prng : PRNG;
function createPRNG() : PRNG{
return prng ||= new MarsagliaPRNG( seeder.getSeed() );
}
}
class TimeSeeder implements Seeder{
function getSeed():Number{
return now();
}
}
//usage:
seeder : Seeder = new TimeSeeder();
prngFactory : PRNGFactory = new SingletonMarsagliaPRNGFactory();
clientA.prng = prngFactory.createPRNG();
clientB.prng = prngFactory.createPRNG();
//both clients got the same instance.
The big advantage is now that if you want/need to change any of the implementation details, nothing has to change in the clients. You can change seeding method, RNG algorithm and the instantiation rule w/o having to touch any client anywhere.

using one random engine for multi distributions in c++11

I am using c++11 new <random> header in my application and in one class in different methods I need different random number with different distributions. I just put a random engine std::default_random_engine as class member seed it in the class constructor with std::random_device and use it for different distributions in my methods. Is that OK to use the random engine in this way or I should declare different engines for every distribution I use.
It's ok.
Reasons to not share the generator:
threading (standard RNG implementations are not thread safe)
determinism of random sequences:
If you wish to be able (for testing/bug hunting) to control the exact sequences generated, you will by likely have fewer troubles by isolating the RNGs used, especially when not all RNGs consumption is deterministic.
You should be careful when using one pseudo random number generator for different random variables, because in doing so they become correlated.
Here is an example: If you want to simulate Brownian motion in two dimensions (e.g. x and y) you need randomness in both dimensions. If you take the random numbers from one generator (noise()) and assign them successively
while(simulating)
x = x + noise()
y = y + noise()
then the variables x and y become correlated, because the algorithms of the pseudo number generators only make statements about how good they are, if you take every single number generated and not only every second one like in this example. Here, the Brownian particles could maybe move into the positive x and y directions with a higher probability than in the negative directions and thus introduce an artificial drift.
For two further reasons to use different generators look at sehe's answer.
MosteM's answer isn't correct. It's correct to do this so long as you want the draws from the distributions to be independent. If for some reason you need exactly the same random input into draws of different distributions, then you may want different RNGs. If you want correlation between two random variables, it's better to build them starting from a common random variable using mathematical principal: e.g., if A, B are independent normal(0,1), then A and aA +sqrt(1-a**2)B are normal(0,1) with correlation a.
EDIT: I found a great resource on the C++11 random library which may be useful to you.
There is no reason not to do it like this. Depending on which random generator you use, the period is quite huge (2^19937 in case of Mersenne-Twister), so in most cases, you won't even reach the end of one period during the execution of your program. And even if it is not said that, it's worse to reach the period with all distributions using the same generator than having 3 generators each doing 1/3 of their period.
In my programs, I use one generator for each thread, and it works fine. I think that's the main reason they split up the generator and distributions in C++11, since if you weren't allowed to do this, there would be no benefit from having the generator and the distribution separate, if one needs one generator for each distribution anyway.

random_shuffle algorithm - are identical results produced without random generator function?

If a random generator function is not supplied to the random_shuffle algorithm in the standard library, will successive runs of the program produce the same random sequence if supplied with the same data?
For example, if
std::random_shuffle(filenames.begin(), filenames.end());
is performed on the same list of filenames from a directory in successive runs of the program, is the random sequence produced the same as that in the prior run?
If you use the same random generator, with the same seed, and the same starting
sequence, the results will be the same. A computer is, after all,
deterministic in its behavior (modulo threading issues and a few other
odds and ends).
If you do not specify a generator, the default generator is
implementation defined. Most implementations, I think, use
std::rand() (which can cause problems, particularly when the number of
elements in the sequence is larger than RAND_MAX). I would recommend
getting a generator with known quality, and using it.
If you don't correctly seed the generator which is being used (another
reason to not use the default, since how you seed it will depend on the
implementation), then you'll get what you get. In the case of
std::rand(), the default always uses the same seed. How you seed
depends on the generator used. What you use to seed it should be vary
from one run to the other; for many applications, time(NULL) is
sufficient; on a Unix platform, I'd recommend reading however many bytes
it takes from /dev/random. Otherwise, hashing other information (IP
address of the machine, process id, etc.) can also improve things---it
means that two users starting the program at exactly the same second
will still get different sequences. (But this is really only relevant
if you're working in a networked environment.)
25.2.11 just says that the elements are shuffled with uniform distribution. It makes no guarantees as to which RNG is used behind the scenes (unless you pass one in) so you can't rely on any such behavior.
In order to guarantee the same shuffle outcome you'll need to provide your own RNG that provides those guarantees, but I suspect even then if you update your standard library the random_shuffle algorithm itself could change effects.
You may produce an identical result every run of the program. You can add a custom random number generator (which can be seeded from an external source) as an additional argument to std::random_shuffle if this is a problem. The function would be the third argument. Some people recommend call srand(unsigned(time(NULL))); before random_shuffle, but the results are often times implementation defined (and unreliable).