For example [Disclaimer: it is a non-tested function]. Following Roger Dahl's comment, I have done a comparison Kepler K20c between four different possibilities for the generation of arrays of elements:.
Using a different seed on each run should be straightforward. You can create more than one global function for random number initialization and generation.
This does nt have any noticeable effect on the random number generated. But there is a fear of not being correlated and also lack of convergence in the long run.
Why would you like to seed more than once in an iteration anyways. Learn more. Ask Question. Asked 6 years, 1 month ago. Active 2 years, 1 month ago. Viewed 8k times. You can use a single master seed and then offset into the sequence according to thread index. Note that simply using different seeds to the same generator is not statistically robust, you should use a single seed and then offset into the sequence to get distinct sub-sequences.
Python random.seed() function to initialize the pseudo-random number generator
Active Oldest Votes. The timing for generating has been the following: ms ; ms ; ms ; ms ; I hope I have correctly understood the performance issue raised by Roger Dahl. Roger Dahl There are performance implications to the different ways of handling the prng seeding. It is much faster to save and restore random generator state than to recalculate the starting state repeatedly.
RogerDahl I have edited my answer. I hope I have correctly understood the performance issue you have raised. Thank you for investigating the performance issues. I think the main takeaway from that note in the docs is that it's probably a good idea to move the seeding of the PRNGs outside of the loop if possible.
You run that kernel only once when the app starts. Then, inside the loop, you just keep restoring and saving the state. RogerDahl Thanks for adding your own results to my own answer. I think that the above results agree with your last comment in that case 3 has remarkably better performance than case 4. If you have a specific generator for which this will not work, please post more information.Monte Carlo methods provide approximate numerical solutions to problems that would be difficult or impossible to solve exactly.
The defining characteristic of Monte Carlo simulations is the use of multiple independent trials, each driven by some stochastic random process. The results of the independent trials are then combined to extract the average answer, relying on the Law of Large Numbers, which states that as more trials are combined, the average answer will converge on the true answer. The independent trials are inherently parallelizable, and they typically consist of dense numeric operations, so GPUs provide an almost ideal platform for Monte Carlo simulations.
However, a key component within Monte Carlo simulations is the random number generators RNGs that provide the independent stochastic input to each trial.
These generators must meet the conflicting goals of being extremely fast while also providing random number streams that are indistinguishable from a true random number source. There is an extensive body of literature devoted to random number generation in CPUs, but the most efficient of these make fundamental assumptions about processor architecture and performance: they are often not appropriate for use in GPUs.
Previous work such as Sussman et. In this chapter, we discuss methods for generating random numbers using CUDA, with particular regard to generation of Gaussian random numbers, a key component of many financial simulations. We describe two methods for generating Gaussian random numbers, one of which works by transforming uniformly distributed numbers using the Box-Muller method, and another that generates Gaussian distributed random numbers directly using the Wallace method.
We then demonstrate how these random number generators can be used in real simulations, using two examples of valuing exotic options using CUDA. Monte Carlo approaches were introduced by Ulam and von Neumann in the s with the aim of simulating nuclear reactions Metropolis A simple example of a Monte Carlo solution to a problem is for calculating.
Take a square and inscribe within it a circle that touches each edge of the square. We know that if the radius of the circle is rthen the area of the circle is r 2and the area of the square is 4 r 2. If we can calculate the ratio, pof the circle area to the square area, then we can calculate :. We can calculate the ratio p using Monte Carlo methods by generating n independent random points that are uniformly distributed within the square.
As the number of points increases, the accuracy improves, giving estimates of 3. Clearly this is an inefficient way to calculate : the rate of increase in accuracy is low accuracy is usually proportional to the square root of the number of trials and is much less efficient than standard iterative methods.
However, it does demonstrate three reasons why Monte Carlo methods are so popular:. This last point is a major advantage, allowing Monte Carlo solutions to easily scale to multiple nodes in a networked cluster, or to multiple processors within a CPU or GPU. A more realistic example of Monte Carlo methods is in finance. Here the approach is to capture a subset of market variables—for example, the price S 0 of an equity at time 0—then choose an artificial stochastic model that appears to model previous equity paths reasonably well.
A programmatic estimator of the average stock price would then be as shown in Listing The goal of the random runs is to produce an approximation to the behavior of the historical market and use the results to judge the probability of making a profit. This is similar to the computation example, where the system tends toward the probability of a point being within the circle, and that probability is directly related to the ratio of areas.
This method allows us to find solutions for a wide class of financial models for which there are no analytical solutions. The Monte Carlo approach is trivially parallelizable across multiple processors, and it is often called embarrassingly parallel.
There are five major steps:.
In many traditional architectures, the difficult part is step 4, writing an efficient implementation of the simulation kernel, because a faster kernel allows more simulated trials to be executed within a given time and so will provide a more accurate answer. However, in a GPU, the mathematically heavy simulation kernel is often relatively easy to implement.
The real challenge is to manage the assignment of different random number streams to processors, and to generate those random number streams efficiently.Parallel programming is often used for simulation programs which need some element of randomness to work. Monte Carlo programs, which use randomness sampling, are also often written in a parallel manner.
The normal C rand function also has a state, but it is global, and hidden from the programmer. This makes rand not thread-safe, but easier to use. The sequence number is another seed-like value. It is used so that, if all cores have the same seed, but different sequence numbers, then they will get different random values.
The following program uses these functions to generate random numbers:. When run, this program produces the exact same random number each time.
This is because the seed passed in was 0. In order to get a different random number each time, we can pass in the current time as the seed. If we want each run of the program to produce different sequences of random numbers, then we would need to set the seed to the current time. However, now we would likely have each core get the same sequence of numbers. This is probably undesirable. To avoid it, we set the sequence parameter to the thread's ID.
This way, each thread will have a different stream of random numbers, which will also be different each time the program is run.
Source code for torch.cuda.random
In addition to the curand function which, together with modular arithmetic, can return to us random integers from any range we wish, cuRAND provides functions to get floating point numbers from different distributions:.
This means that all floating point numbers in that range are equally likely to be produced. Normal distributions would be important for modelling many natural phenomenon accurately.There are plenty of different methods which allow a programmer to grab a bunch of pseudo random numbers which take into account the accuracy of truly randomised values vs.
CUDA and thrust parallel primitives offer a variety of host and device API methods to generate random numbers, but also provide a good insight into the processing speed comparison vs. So, as more of a technical experiment to start observing the real performance differences, I started running some basic tests on arbitrary size data containers using four different methods the first three use host API calls.
Pretty simple, the point of showing this is to be able to compare the speed of this method to the other three GPU based implementations. The second is slightly more complicated, but is from what I can see the easiest way of creating a device vector of random numbers using the GPU without using curand.
Here, we use a tranformation of counting iterators to calculate a value per element of the rng sequence. The operator in this case can be defined with our own structure, hence the choice of an applied uniform distribution in this implemention. This method gives a the ability to easily change the limits and way the random numbers are created, however as you will see below, a simple curand implemention will do the same. Here, we need to allocate space for each individual kernels curandState as well as the device container for the random results.
You can copy the generator state used in local memory for fast generation, and store it back in global memory between kernel launches. Now the interesting part; how long do each of these methods take to perform for a variety of random numbers! The test cases I have run always apply a uniform distribution and generate numbers between 0.
In general, you will get the best performance from cuRand by generating containers of random numbers that are as large as possible. The docs on the cuRand library are really good and the examples worth looking at for more info. You are commenting using your WordPress. You are commenting using your Google account.
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Sign up.LTRAC: C# Random Number Seeds
Branch: master. Find file Copy path. Cannot retrieve contributors at this time. Raw Blame History. There are also no function type signatures to ensure that compilation is deferred so that import is quick, and Sphinx autodoc works. This ensures that manually set small seeds don't result in a predictable initial sequence from the random number generator. This advances the RNG sequence by two steps.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. The implementations below are based on the C source code:. NumPy casting conventions which cast uint64 [op] int32 to float64 don't.
There are also no function type signatures to ensure that compilation is. We are also. This ensures that manually set small seeds don't result in a predictable.
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Box-Muller transform. This initializes the RNG states so that each state in the array corresponds.Thanks for the quick reply. I have tried both. And i think I would go ahead with the second option. Thanks again. Thanks for the tutorial! Don't you have any performance issues generating the curandState vector? Thank you!!! I hope I answered your question. Feel free to ask otherwise. In such way i can save some time instead of spending it to invoke "init" kernel.
About the sequence number, it ensures that the states in various threads won't have any correlation. If you use the same seed in each thread then the successions will be identical. By specifying a sequence number here referred to the thread index your successions "will not have statistically correlated values". This is very informative blog. Do i have to keep this code in a while loop if i need to generate random numbers in each run of the code? Hello and well done for your great Job.
I used your curand code and made some measurements 3 very large data structures float numbers and it almost took one minute to produce the pseudo random numbers! Do you know why is this might happening? Thanks beforehand. Sumati: It depends on how you have implemented your code. Can you post an example? RNG is a computationally expensive task, btw.
This has caused me some headaches in the past I am wondering how to generate 2 random numbers per thread? You are my hero, dude. Thanks Man! Is there any ohter option? Sorry if I am asking a foolish question, but I did not get the line "dim3 tpb N,1,1 ". Could someone kindly explain? Whenever you launch a kernel you must specify how many threads per block i.
In this oversimplified and non-compiling example, I create a single block with N threads per block. Naturally, you want to replace N with an arbitrary number of threads, if you really want to compile this example. What is really important is that you get the relationship between N and the rest of the data structures. Post a Comment.
Newer Post Older Post Home.The device API includes functions pseudorandom generation for and quasirandom generation. The functions for pseudorandom sequences support bit generation and generation from distributions.
If curand is called with the same initial state each time, and the state is not modified between the calls to curandthe same sequence is always generated. Different seeds are guaranteed to produce different starting states and different sequences.
The same seed always produces the same state and the same sequence. Sequences generated with different seeds usually do not have statistically correlated values, but some choices of seeds may give statistically correlated sequences. Sequences generated with the same seed and different sequence numbers will not have statistically correlated values. For the highest quality parallel pseudorandom number generation, each experiment should be assigned a unique seed. Within an experiment, each thread of computation should be assigned a unique sequence number.
If an experiment spans multiple kernel launches, it is recommended that threads between kernel launches be given the same seed, and sequence numbers be assigned in a monotonically increasing way. If the same configuration of threads is launched, random state can be preserved in global memory between launches to avoid state setup time. In this algorithm, samples are generated for multiple sequences, each sequence based on a set of computed parameters.
There is one state structure for each parameter set sequenceand the algorithm allows thread-safe generation and state update for up to concurrent threads within a single block for each of the sequences. Note that two different blocks can not operate on the same state safely.
Also note that, within a block, at most threads may operate on a given state. For the MTGP32 generator, two host functions are provided to help set up parameters for the different sequences in device memory, and to set up the initial state. This function initializes n states, based on the specified parameter set and seed, and copies them to device memory indicated by s. Note that if you are using the pre-generated states, the maximum value of n is This function computes a thread index, and for that index generates a result and updates state.
The thread index t is computed as:. This function may be called repeatedly from a single kernel launch, with the following constraints:. This function generates a result and updates state for the position specified by a thread-specific indexand advances the offset in the state by n positions.
Figure 1 is an illustration of how blocks and threads in MTGP32 operate on the generator states. Each row represents a circular state array of bit integers s n. Threads operating on the array are identified as T m. The specific case shown matches the internal implementation of the host API, which launches 64 blocks of threads. Each block operates on a different sequence, determined by a unique set of parameters, P n. One complete state of an MTGP32 sequence is defined by bit integers.
After thread synchronization, the base index n is advanced by the number of threads that have updated the state.
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