cuRAND

Provides pseudo-random number generator (PRNG) and quasi-random generator (QRNG). See NVIDIA cuRAND.

class PRNG

class pyculib.rand.PRNG(rndtype=100, seed=None, offset=None, stream=None)

cuRAND pseudo random number generator

Parameters:
  • rndtype – Algorithm type. All possible values are listed as class attributes of this class, e.g. TEST, DEFAULT, XORWOW, MRG32K3A, MTGP32.
  • seed – Seed for the RNG.
  • offset – Offset to the random number stream.
  • stream – CUDA stream.

Example:

>>> from pyculib import rand
>>> from numpy import empty
>>> prng = rand.PRNG(rndtype=rand.PRNG.XORWOW)
>>> r = empty(10)
>>> prng.uniform(r)
>>> r
array([ ... ])
lognormal(ary, mean, sigma, size=None)
Generate floating point random number sampled
from a log-normal distribution and fill into ary.
Parameters:
  • ary – Numpy array or cuda device array.
  • mean – Center of the distribution.
  • sigma – Standard deviation of the distribution.
  • size – Number of samples. Default to array size.
normal(ary, mean, sigma, size=None)

Generate floating point random number sampled from a normal distribution and fill into ary.

Parameters:
  • ary – Numpy array or cuda device array.
  • mean – Center of the distribution.
  • sigma – Standard deviation of the distribution.
  • size – Number of samples. Default to array size.
poisson(ary, lmbd, size=None)

Generate floating point random number sampled from a poisson distribution and fill into ary.

Parameters:
  • ary – Numpy array or cuda device array.
  • lmbda – Lambda for the distribution.
  • size – Number of samples. Default to array size.
seed

Mutatable attribute for the seed for the RNG

uniform(ary, size=None)
Generate floating point random number sampled
from a uniform distribution and fill into ary.
Parameters:
  • ary – Numpy array or cuda device array.
  • size – Number of samples. Default to array size.

class QRNG

class pyculib.rand.QRNG(rndtype=200, ndim=None, offset=None, stream=None)

cuRAND quasi random number generator

Parameters:
  • rndtype – Algorithm type. Also control output data type. All possible values are listed as class attributes of this class, e.g. TEST, DEFAULT, SOBOL32, SCRAMBLED_SOBOL32, SOBOL64, SCRAMABLED_SOBOL64.
  • ndim – Number of dimension for the QRNG.
  • offset – Offset to the random number stream.
  • stream – CUDA stream.
generate(ary, size=None)

Generate quasi random number in ary.

Parameters:
  • ary – Numpy array or cuda device array.
  • size – Number of samples; Default to array size. Must be multiple of ndim.
ndim

Mutatable attribute for number of dimension for the QRNG.

Top Level PRNG Functions

Simple interface to the PRNG methods.

Note

This methods automatically create a PRNG object.

pyculib.rand.uniform(size, dtype=<class 'float'>, device=False)

Generate floating point random number sampled from a uniform distribution

Parameters:
  • size – Number of samples.
  • dtype – np.float32 or np.float64.
  • device – Set to True to return a device array instead or numpy array.
Returns:

A numpy array or a device array.

 
>>> from pyculib import rand
>>> rand.uniform(size=10)
array([...])

See also

pyculib.rand.PRNG.uniform()

pyculib.rand.normal(mean, sigma, size, dtype=<class 'float'>, device=False)

Generate floating point random number sampled from a normal distribution

Parameters:
  • mean – Center point of the distribution.
  • sigma – Standard deviation of the distribution.
  • size – — Number of samples.
  • dtype – np.float32 or np.float64.
  • device – Set to True to return a device array instead or ndarray.
Returns:

A numpy array or a device array.

 
>>> from pyculib import rand
>>> rand.normal(mean=0, sigma=1, size=10)
array([...])

See also

pyculib.rand.PRNG.normal()

pyculib.rand.lognormal(mean, sigma, size, dtype=<class 'float'>, device=False)

Generate floating point random number sampled from a log-normal distribution.

Parameters:
  • mean – Center point of the distribution.
  • sigma – Standard deviation of the distribution.
  • size – Number of samples.
  • dtype – np.float32 or np.float64.
  • device – set to True to return a device array instead or ndarray.
Returns:

A numpy array or a device array.

 
>>> from pyculib import rand
>>> rand.lognormal(mean=0, sigma=1, size=10)
array([...])

See also

pyculib.rand.PRNG.lognormal()

pyculib.rand.poisson(lmbd, size, device=False)

Generate int32 random number sampled from a poisson distribution.

Parameters:
  • lmbda – Lambda of the distribution.
  • size – Number of samples
  • device – Set to True to return a device array instead or ndarray.
Returns:

A numpy array or a device array.

 
>>> from pyculib import rand
>>> rand.poisson(lmbd=1, size=10)
array([...], dtype=uint32)

See also

pyculib.rand.PRNG.poisson()

Top Level QRNG Functions

Simple interface to the QRNG methods.

Note

This methods automatically create a QRNG object.

pyculib.rand.quasi(size, bits=32, nd=1, device=False)

Generate quasi random number using SOBOL{bits} RNG type.

Parameters:
  • size – Number of samples.
  • bits – Bit length of output element; e.g. 32 or 64.
  • nd – Number of dimension .
  • device – Set to True to return a device array instead or ndarray.
Returns:

A numpy array or a device array.

 
>>> from pyculib import rand
>>> rand.quasi(10)
array([...], dtype=uint32)

See also

pyculib.rand.QRNG.generate()