MRG32k3a generator
E760432
MRG (multiple recursive generator)
combined multiple recursive generator
pseudorandom number generator
The MRG32k3a generator is a high-quality combined multiple recursive pseudorandom number generator widely used in scientific computing and simulations for its long period and good statistical properties.
All labels observed (2)
| Label | Occurrences |
|---|---|
| MRG32k3a generator canonical | 1 |
| RngStreams library | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8823537 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MRG32k3a generator Context triple: [cuRAND, provides, MRG32k3a generator]
-
A.
MersenneTwister
MersenneTwister is a widely used pseudorandom number generator algorithm known for its long period and high-quality statistical properties.
-
B.
Blum–Blum–Shub pseudorandom number generator
The Blum–Blum–Shub pseudorandom number generator is a cryptographically secure generator based on the hardness of factoring large composite numbers, widely studied in theoretical computer science and cryptography.
-
C.
Blum–Micali pseudorandom number generator
The Blum–Micali pseudorandom number generator is a foundational cryptographic algorithm that produces provably secure pseudorandom bits based on number-theoretic hardness assumptions.
-
D.
cuRAND
cuRAND is NVIDIA's GPU-accelerated random number generation library designed to efficiently produce high-quality random numbers for parallel applications using CUDA.
-
E.
Berlekamp–Massey algorithm
The Berlekamp–Massey algorithm is a key algorithm in coding theory and cryptography used to efficiently determine the shortest linear feedback shift register that generates a given binary sequence.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MRG32k3a generator Target entity description: The MRG32k3a generator is a high-quality combined multiple recursive pseudorandom number generator widely used in scientific computing and simulations for its long period and good statistical properties.
-
A.
MersenneTwister
MersenneTwister is a widely used pseudorandom number generator algorithm known for its long period and high-quality statistical properties.
-
B.
Blum–Blum–Shub pseudorandom number generator
The Blum–Blum–Shub pseudorandom number generator is a cryptographically secure generator based on the hardness of factoring large composite numbers, widely studied in theoretical computer science and cryptography.
-
C.
Blum–Micali pseudorandom number generator
The Blum–Micali pseudorandom number generator is a foundational cryptographic algorithm that produces provably secure pseudorandom bits based on number-theoretic hardness assumptions.
-
D.
cuRAND
cuRAND is NVIDIA's GPU-accelerated random number generation library designed to efficiently produce high-quality random numbers for parallel applications using CUDA.
-
E.
Berlekamp–Massey algorithm
The Berlekamp–Massey algorithm is a key algorithm in coding theory and cryptography used to efficiently determine the shortest linear feedback shift register that generates a given binary sequence.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
MRG (multiple recursive generator)
ⓘ
combined multiple recursive generator ⓘ pseudorandom number generator ⓘ |
| hasApplication |
Monte Carlo simulation
ⓘ
financial risk simulation ⓘ queueing simulations ⓘ scientific computing ⓘ stochastic modeling ⓘ |
| hasAuthor | Pierre L’Ecuyer ⓘ |
| hasCategory |
multiple recursive generator with combination
ⓘ
uniform random number generator ⓘ |
| hasCombinationRule | z_n = (x_n - y_n) mod 4294967087 ⓘ |
| hasComponentCount | 2 ⓘ |
| hasComponentType | 3rd-order multiple recursive generator ⓘ |
| hasDesignGoal |
high-quality random variates for simulation
ⓘ
support for parallel and distributed simulations ⓘ |
| hasDimension | 6 ⓘ |
| hasFeature |
ability to jump ahead in the sequence
ⓘ
well-defined stream and substream structure ⓘ |
| hasModulus |
4294944443
ⓘ
4294967087 ⓘ |
| hasOutputRange | (0,1) ⓘ |
| hasOutputType | double-precision uniform variates ⓘ |
| hasPeriodLength | approximately 2^191 ⓘ |
| hasProperty |
good equidistribution properties
ⓘ
good statistical quality ⓘ long period ⓘ supports efficient substream generation ⓘ supports multiple independent streams ⓘ |
| hasRecurrence |
x_n = (1403580 x_{n-2} - 810728 x_{n-3}) mod 4294967087
ⓘ
y_n = (527612 y_{n-1} - 1370589 y_{n-3}) mod 4294944443 ⓘ |
| hasSeedConstraint | state must not be all zeros in each component ⓘ |
| hasStateSize | 6 integers ⓘ |
| hasStreamCountPerSeed | 2^64 streams (conceptual design) ⓘ |
| hasSubstreamCountPerStream | 2^64 substreams (conceptual design) ⓘ |
| hasYearIntroduced | late 1990s ⓘ |
| isAlternativeTo |
Mersenne Twister
ⓘ
linked to:
MersenneTwister
linear congruential generators ⓘ |
| isDescribedIn | Pierre L’Ecuyer’s papers on combined multiple recursive generators ⓘ |
| isImplementedIn |
Intel Math Kernel Library
ⓘ
MATLAB random number generation toolbox ⓘ NAG Library ⓘ Python (via various simulation libraries) ⓘ R (via external packages and interfaces) ⓘ RngStreams library ⓘ
linked to:
MRG32k3a generator
SSJ (Stochastic Simulation in Java) library ⓘ |
| isPreferredFor | high-precision simulation studies ⓘ |
| passesTestSuite | TestU01 Crush battery (under recommended usage) ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: MRG32k3a generator Description of subject: The MRG32k3a generator is a high-quality combined multiple recursive pseudorandom number generator widely used in scientific computing and simulations for its long period and good statistical properties.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: MRG32k3a generator