Host API

Macros

CURANDAPI

CURAND_VERSION

CURAND_VER_BUILD

CURAND_VER_MAJOR

CURAND_VER_MINOR

CURAND_VER_PATCH

Enumerations

curandDirectionVectorSet

CURAND choice of direction vector set.

curandOrdering

CURAND ordering of results in memory.

curandRngType

CURAND generator types.

curandStatus

CURAND function call status types.

Functions

curandStatus_t curandCreateGenerator(curandGenerator_t *generator, curandRngType_t rng_type)

Create new random number generator.

curandStatus_t curandCreateGeneratorHost(curandGenerator_t *generator, curandRngType_t rng_type)

Create new host CPU random number generator.

curandStatus_t curandCreatePoissonDistribution(double lambda, curandDiscreteDistribution_t *discrete_distribution)

Construct the histogram array for a Poisson distribution.

curandStatus_t curandDestroyDistribution(curandDiscreteDistribution_t discrete_distribution)

Destroy the histogram array for a discrete distribution (e.g.

curandStatus_t curandDestroyGenerator(curandGenerator_t generator)

Destroy an existing generator.

curandStatus_t curandGenerate(curandGenerator_t generator, unsigned int *outputPtr, size_t num)

Generate 32-bit pseudo or quasirandom numbers.

curandStatus_t curandGenerateBinomial(curandGenerator_t generator, unsigned int *outputPtr, size_t num, unsigned int n, double p)

curandStatus_t curandGenerateBinomialMethod(curandGenerator_t generator, unsigned int *outputPtr, size_t num, unsigned int n, double p, curandMethod_t method)

curandStatus_t curandGenerateLogNormal(curandGenerator_t generator, float *outputPtr, size_t n, float mean, float stddev)

Generate log-normally distributed floats.

curandStatus_t curandGenerateLogNormalDouble(curandGenerator_t generator, double *outputPtr, size_t n, double mean, double stddev)

Generate log-normally distributed doubles.

curandStatus_t curandGenerateLongLong(curandGenerator_t generator, unsigned long long *outputPtr, size_t num)

Generate 64-bit quasirandom numbers.

curandStatus_t curandGenerateNormal(curandGenerator_t generator, float *outputPtr, size_t n, float mean, float stddev)

Generate normally distributed doubles.

curandStatus_t curandGenerateNormalDouble(curandGenerator_t generator, double *outputPtr, size_t n, double mean, double stddev)

Generate normally distributed doubles.

curandStatus_t curandGeneratePoisson(curandGenerator_t generator, unsigned int *outputPtr, size_t n, double lambda)

Generate Poisson-distributed unsigned ints.

curandStatus_t curandGeneratePoissonMethod(curandGenerator_t generator, unsigned int *outputPtr, size_t n, double lambda, curandMethod_t method)

curandStatus_t curandGenerateSeeds(curandGenerator_t generator)

Setup starting states.

curandStatus_t curandGenerateUniform(curandGenerator_t generator, float *outputPtr, size_t num)

Generate uniformly distributed floats.

curandStatus_t curandGenerateUniformDouble(curandGenerator_t generator, double *outputPtr, size_t num)

Generate uniformly distributed doubles.

curandStatus_t curandGetDirectionVectors32(curandDirectionVectors32_t *vectors[], curandDirectionVectorSet_t set)

Get direction vectors for 32-bit quasirandom number generation.

curandStatus_t curandGetDirectionVectors64(curandDirectionVectors64_t *vectors[], curandDirectionVectorSet_t set)

Get direction vectors for 64-bit quasirandom number generation.

curandStatus_t curandGetProperty(libraryPropertyType type, int *value)

Return the value of the curand property.

curandStatus_t curandGetScrambleConstants32(unsigned int **constants)

Get scramble constants for 32-bit scrambled Sobol' .

curandStatus_t curandGetScrambleConstants64(unsigned long long **constants)

Get scramble constants for 64-bit scrambled Sobol' .

curandStatus_t curandGetVersion(int *version)

Return the version number of the library.

curandStatus_t curandSetGeneratorOffset(curandGenerator_t generator, unsigned long long offset)

Set the absolute offset of the pseudo or quasirandom number generator.

curandStatus_t curandSetGeneratorOrdering(curandGenerator_t generator, curandOrdering_t order)

Set the ordering of results of the pseudo or quasirandom number generator.

curandStatus_t curandSetPseudoRandomGeneratorSeed(curandGenerator_t generator, unsigned long long seed)

Set the seed value of the pseudo-random number generator.

curandStatus_t curandSetQuasiRandomGeneratorDimensions(curandGenerator_t generator, unsigned int num_dimensions)

Set the number of dimensions.

curandStatus_t curandSetStream(curandGenerator_t generator, cudaStream_t stream)

Set the current stream for CURAND kernel launches.

Macros

CURANDAPI
CURAND_VERSION

(CURAND_VER_MAJOR * 1000 + \

CURAND_VER_MINOR *  100 + \

CURAND_VER_PATCH)


CURAND_VER_BUILD 48
CURAND_VER_MAJOR 10
CURAND_VER_MINOR 4
CURAND_VER_PATCH 4

Enumerations

enum curandDirectionVectorSet

CURAND choice of direction vector set.

Values:

enumerator CURAND_DIRECTION_VECTORS_32_JOEKUO6

Specific set of 32-bit direction vectors generated from polynomials recommended by S. Joe and F. Y. Kuo, for up to 20,000 dimensions.

enumerator CURAND_SCRAMBLED_DIRECTION_VECTORS_32_JOEKUO6

Specific set of 32-bit direction vectors generated from polynomials recommended by S. Joe and F. Y. Kuo, for up to 20,000 dimensions, and scrambled.

enumerator CURAND_DIRECTION_VECTORS_64_JOEKUO6

Specific set of 64-bit direction vectors generated from polynomials recommended by S. Joe and F. Y. Kuo, for up to 20,000 dimensions.

enumerator CURAND_SCRAMBLED_DIRECTION_VECTORS_64_JOEKUO6

Specific set of 64-bit direction vectors generated from polynomials recommended by S. Joe and F. Y. Kuo, for up to 20,000 dimensions, and scrambled.

enum curandOrdering

CURAND ordering of results in memory.

Values:

enumerator CURAND_ORDERING_PSEUDO_BEST

Best ordering for pseudorandom results.

enumerator CURAND_ORDERING_PSEUDO_DEFAULT

Specific default thread sequence for pseudorandom results, same as CURAND_ORDERING_PSEUDO_BEST.

enumerator CURAND_ORDERING_PSEUDO_SEEDED

Specific seeding pattern for fast lower quality pseudorandom results.

enumerator CURAND_ORDERING_PSEUDO_LEGACY

Specific legacy sequence for pseudorandom results, guaranteed to remain the same for all cuRAND release.

enumerator CURAND_ORDERING_PSEUDO_DYNAMIC

Specific ordering adjusted to the device it is being executed on, provides the best performance.

enumerator CURAND_ORDERING_QUASI_DEFAULT

Specific n-dimensional ordering for quasirandom results.

enum curandRngType

CURAND generator types.

Values:

enumerator CURAND_RNG_TEST
enumerator CURAND_RNG_PSEUDO_DEFAULT

Default pseudorandom generator.

enumerator CURAND_RNG_PSEUDO_XORWOW

XORWOW pseudorandom generator.

enumerator CURAND_RNG_PSEUDO_MRG32K3A

MRG32k3a pseudorandom generator.

enumerator CURAND_RNG_PSEUDO_MTGP32

Mersenne Twister MTGP32 pseudorandom generator.

enumerator CURAND_RNG_PSEUDO_MT19937

Mersenne Twister MT19937 pseudorandom generator.

enumerator CURAND_RNG_PSEUDO_PHILOX4_32_10

PHILOX-4x32-10 pseudorandom generator.

enumerator CURAND_RNG_QUASI_DEFAULT

Default quasirandom generator.

enumerator CURAND_RNG_QUASI_SOBOL32

Sobol32 quasirandom generator.

enumerator CURAND_RNG_QUASI_SCRAMBLED_SOBOL32

Scrambled Sobol32 quasirandom generator.

enumerator CURAND_RNG_QUASI_SOBOL64

Sobol64 quasirandom generator.

enumerator CURAND_RNG_QUASI_SCRAMBLED_SOBOL64

Scrambled Sobol64 quasirandom generator.

enum curandStatus

CURAND function call status types.

Values:

enumerator CURAND_STATUS_SUCCESS

No errors.

enumerator CURAND_STATUS_VERSION_MISMATCH

Header file and linked library version do not match.

enumerator CURAND_STATUS_NOT_INITIALIZED

Generator not initialized.

enumerator CURAND_STATUS_ALLOCATION_FAILED

Memory allocation failed.

enumerator CURAND_STATUS_TYPE_ERROR

Generator is wrong type.

enumerator CURAND_STATUS_OUT_OF_RANGE

Argument out of range.

enumerator CURAND_STATUS_LENGTH_NOT_MULTIPLE

Length requested is not a multple of dimension.

enumerator CURAND_STATUS_DOUBLE_PRECISION_REQUIRED

GPU does not have double precision required by MRG32k3a.

enumerator CURAND_STATUS_LAUNCH_FAILURE

Kernel launch failure.

enumerator CURAND_STATUS_PREEXISTING_FAILURE

Preexisting failure on library entry.

enumerator CURAND_STATUS_INITIALIZATION_FAILED

Initialization of CUDA failed.

enumerator CURAND_STATUS_ARCH_MISMATCH

Architecture mismatch, GPU does not support requested feature.

enumerator CURAND_STATUS_INTERNAL_ERROR

Internal library error.

Functions

curandStatus_t curandCreateGenerator(curandGenerator_t *generator, curandRngType_t rng_type)

Create new random number generator.

Creates a new random number generator of type rng_type and returns it in *generator.

Legal values for rng_type are:

  • CURAND_RNG_PSEUDO_DEFAULT

  • CURAND_RNG_PSEUDO_XORWOW

  • CURAND_RNG_PSEUDO_MRG32K3A

  • CURAND_RNG_PSEUDO_MTGP32

  • CURAND_RNG_PSEUDO_MT19937

  • CURAND_RNG_PSEUDO_PHILOX4_32_10

  • CURAND_RNG_QUASI_DEFAULT

  • CURAND_RNG_QUASI_SOBOL32

  • CURAND_RNG_QUASI_SCRAMBLED_SOBOL32

  • CURAND_RNG_QUASI_SOBOL64

  • CURAND_RNG_QUASI_SCRAMBLED_SOBOL64

When rng_type

is CURAND_RNG_PSEUDO_DEFAULT, the type chosen is CURAND_RNG_PSEUDO_XORWOW.

When

rng_type is CURAND_RNG_QUASI_DEFAULT, the type chosen is CURAND_RNG_QUASI_SOBOL32.

The default values for rng_type = CURAND_RNG_PSEUDO_XORWOW are:

  • seed = 0

  • offset = 0

  • ordering = CURAND_ORDERING_PSEUDO_DEFAULT

The default values for rng_type = CURAND_RNG_PSEUDO_MRG32K3A are:

  • seed = 0

  • offset = 0

  • ordering = CURAND_ORDERING_PSEUDO_DEFAULT

The default values for rng_type = CURAND_RNG_PSEUDO_MTGP32 are:

  • seed = 0

  • offset = 0

  • ordering = CURAND_ORDERING_PSEUDO_DEFAULT

The default values for rng_type = CURAND_RNG_PSEUDO_MT19937 are:

  • seed = 0

  • offset = 0

  • ordering = CURAND_ORDERING_PSEUDO_DEFAULT

  • The default values for rng_type = CURAND_RNG_PSEUDO_PHILOX4_32_10 are:

  • seed = 0

  • offset = 0

  • ordering = CURAND_ORDERING_PSEUDO_DEFAULT

The default values for rng_type = CURAND_RNG_QUASI_SOBOL32 are:

  • dimensions = 1

  • offset = 0

  • ordering = CURAND_ORDERING_QUASI_DEFAULT

The default values for rng_type = CURAND_RNG_QUASI_SOBOL64 are:

  • dimensions = 1

  • offset = 0

  • ordering = CURAND_ORDERING_QUASI_DEFAULT

The default values for rng_type = CURAND_RNG_QUASI_SCRAMBBLED_SOBOL32 are:

  • dimensions = 1

  • offset = 0

  • ordering = CURAND_ORDERING_QUASI_DEFAULT

The default values for rng_type = CURAND_RNG_QUASI_SCRAMBLED_SOBOL64 are:

  • dimensions = 1

  • offset = 0

  • ordering = CURAND_ORDERING_QUASI_DEFAULT

Parameters

  • generator – - Pointer to generator

  • rng_type – - Type of generator to create

Returns

  • CURAND_STATUS_ALLOCATION_FAILED, if memory could not be allocated

  • CURAND_STATUS_INITIALIZATION_FAILED if there was a problem setting up the GPU

  • CURAND_STATUS_VERSION_MISMATCH if the header file version does not match the dynamically linked library version

  • CURAND_STATUS_TYPE_ERROR if the value for rng_type is invalid

  • CURAND_STATUS_SUCCESS if generator was created successfully

curandStatus_t curandCreateGeneratorHost(curandGenerator_t *generator, curandRngType_t rng_type)

Create new host CPU random number generator.

Creates a new host CPU random number generator of type rng_type and returns it in *generator.

Legal values for rng_type are:

  • CURAND_RNG_PSEUDO_DEFAULT

  • CURAND_RNG_PSEUDO_XORWOW

  • CURAND_RNG_PSEUDO_MRG32K3A

  • CURAND_RNG_PSEUDO_MTGP32

  • CURAND_RNG_PSEUDO_MT19937

  • CURAND_RNG_PSEUDO_PHILOX4_32_10

  • CURAND_RNG_QUASI_DEFAULT

  • CURAND_RNG_QUASI_SOBOL32

  • CURAND_RNG_QUASI_SCRAMBLED_SOBOL32

  • CURAND_RNG_QUASI_SOBOL64

  • CURAND_RNG_QUASI_SCRAMBLED_SOBOL64

When rng_type

is CURAND_RNG_PSEUDO_DEFAULT, the type chosen is CURAND_RNG_PSEUDO_XORWOW.

When

rng_type is CURAND_RNG_QUASI_DEFAULT, the type chosen is CURAND_RNG_QUASI_SOBOL32.

The default values for rng_type = CURAND_RNG_PSEUDO_XORWOW are:

  • seed = 0

  • offset = 0

  • ordering = CURAND_ORDERING_PSEUDO_DEFAULT

The default values for rng_type = CURAND_RNG_PSEUDO_MRG32K3A are:

  • seed = 0

  • offset = 0

  • ordering = CURAND_ORDERING_PSEUDO_DEFAULT

The default values for rng_type = CURAND_RNG_PSEUDO_MTGP32 are:

  • seed = 0

  • offset = 0

  • ordering = CURAND_ORDERING_PSEUDO_DEFAULT

The default values for rng_type = CURAND_RNG_PSEUDO_MT19937 are:

  • seed = 0

  • offset = 0

  • ordering = CURAND_ORDERING_PSEUDO_DEFAULT

  • The default values for rng_type = CURAND_RNG_PSEUDO_PHILOX4_32_10 are:

  • seed = 0

  • offset = 0

  • ordering = CURAND_ORDERING_PSEUDO_DEFAULT

The default values for rng_type = CURAND_RNG_QUASI_SOBOL32 are:

  • dimensions = 1

  • offset = 0

  • ordering = CURAND_ORDERING_QUASI_DEFAULT

The default values for rng_type = CURAND_RNG_QUASI_SOBOL64 are:

  • dimensions = 1

  • offset = 0

  • ordering = CURAND_ORDERING_QUASI_DEFAULT

The default values for rng_type = CURAND_RNG_QUASI_SCRAMBLED_SOBOL32 are:

  • dimensions = 1

  • offset = 0

  • ordering = CURAND_ORDERING_QUASI_DEFAULT

The default values for rng_type = CURAND_RNG_QUASI_SCRAMBLED_SOBOL64 are:

  • dimensions = 1

  • offset = 0

  • ordering = CURAND_ORDERING_QUASI_DEFAULT

Parameters

  • generator – - Pointer to generator

  • rng_type – - Type of generator to create

Returns

  • CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated

  • CURAND_STATUS_INITIALIZATION_FAILED if there was a problem setting up the GPU

  • CURAND_STATUS_VERSION_MISMATCH if the header file version does not match the dynamically linked library version

  • CURAND_STATUS_TYPE_ERROR if the value for rng_type is invalid

  • CURAND_STATUS_SUCCESS if generator was created successfully

curandStatus_t curandCreatePoissonDistribution(double lambda, curandDiscreteDistribution_t *discrete_distribution)

Construct the histogram array for a Poisson distribution.

Construct the histogram array for the Poisson distribution with lambda lambda. For lambda greater than 2000, an approximation with a normal distribution is used.

Parameters

  • lambda – - lambda for the Poisson distribution

  • discrete_distribution – - pointer to the histogram in device memory

Returns

  • CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated

  • CURAND_STATUS_DOUBLE_PRECISION_REQUIRED if the GPU does not support double precision

  • CURAND_STATUS_INITIALIZATION_FAILED if there was a problem setting up the GPU

  • CURAND_STATUS_NOT_INITIALIZED if the distribution pointer was null

  • CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch

  • CURAND_STATUS_OUT_OF_RANGE if lambda is non-positive or greater than 400,000

  • CURAND_STATUS_SUCCESS if the histogram was generated successfully

curandStatus_t curandDestroyDistribution(curandDiscreteDistribution_t discrete_distribution)

Destroy the histogram array for a discrete distribution (e.g.

Poisson).

Destroy the histogram array for a discrete distribution created by curandCreatePoissonDistribution.

Parameters

discrete_distribution – - pointer to device memory where the histogram is stored

Returns

  • CURAND_STATUS_NOT_INITIALIZED if the histogram was never created

  • CURAND_STATUS_SUCCESS if the histogram was destroyed successfully

curandStatus_t curandDestroyGenerator(curandGenerator_t generator)

Destroy an existing generator.

Destroy an existing generator and free all memory associated with its state.

Parameters

generator – - Generator to destroy

Returns

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_SUCCESS if generator was destroyed successfully

curandStatus_t curandGenerate(curandGenerator_t generator, unsigned int *outputPtr, size_t num)

Generate 32-bit pseudo or quasirandom numbers.

Use generator to generate num 32-bit results into the device memory at outputPtr. The device memory must have been previously allocated and be large enough to hold all the results. Launches are done with the stream set using curandSetStream(), or the null stream if no stream has been set.

Results are 32-bit values with every bit random.

Parameters

  • generator – - Generator to use

  • outputPtr – - Pointer to device memory to store CUDA-generated results, or Pointer to host memory to store CPU-generated results

  • num – - Number of random 32-bit values to generate

Returns

  • CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch

  • CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is not a multiple of the quasirandom dimension

  • CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason

  • CURAND_STATUS_TYPE_ERROR if the generator is a 64 bit quasirandom generator. (use curandGenerateLongLong() with 64 bit quasirandom generators)

  • CURAND_STATUS_SUCCESS if the results were generated successfully

curandStatus_t curandGenerateBinomial(curandGenerator_t generator, unsigned int *outputPtr, size_t num, unsigned int n, double p)
curandStatus_t curandGenerateBinomialMethod(curandGenerator_t generator, unsigned int *outputPtr, size_t num, unsigned int n, double p, curandMethod_t method)
curandStatus_t curandGenerateLogNormal(curandGenerator_t generator, float *outputPtr, size_t n, float mean, float stddev)

Generate log-normally distributed floats.

Use generator to generate n float results into the device memory at outputPtr. The device memory must have been previously allocated and be large enough to hold all the results. Launches are done with the stream set using curandSetStream(), or the null stream if no stream has been set.

Results are 32-bit floating point values with log-normal distribution based on an associated normal distribution with mean mean and standard deviation stddev.

Normally distributed results are generated from pseudorandom generators with a Box-Muller transform, and so require n to be even. Quasirandom generators use an inverse cumulative distribution function to preserve dimensionality. The normally distributed results are transformed into log-normal distribution.

There may be slight numerical differences between results generated on the GPU with generators created with curandCreateGenerator() and results calculated on the CPU with generators created with curandCreateGeneratorHost(). These differences arise because of differences in results for transcendental functions. In addition, future versions of CURAND may use newer versions of the CUDA math library, so different versions of CURAND may give slightly different numerical values.

Parameters

  • generator – - Generator to use

  • outputPtr – - Pointer to device memory to store CUDA-generated results, or Pointer to host memory to store CPU-generated results

  • n – - Number of floats to generate

  • mean – - Mean of associated normal distribution

  • stddev – - Standard deviation of associated normal distribution

Returns

  • CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch

  • CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason

  • CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is not a multiple of the quasirandom dimension, or is not a multiple of two for pseudorandom generators

  • CURAND_STATUS_SUCCESS if the results were generated successfully

curandStatus_t curandGenerateLogNormalDouble(curandGenerator_t generator, double *outputPtr, size_t n, double mean, double stddev)

Generate log-normally distributed doubles.

Use generator to generate n double results into the device memory at outputPtr. The device memory must have been previously allocated and be large enough to hold all the results. Launches are done with the stream set using curandSetStream(), or the null stream if no stream has been set.

Results are 64-bit floating point values with log-normal distribution based on an associated normal distribution with mean mean and standard deviation stddev.

Normally distributed results are generated from pseudorandom generators with a Box-Muller transform, and so require n to be even. Quasirandom generators use an inverse cumulative distribution function to preserve dimensionality. The normally distributed results are transformed into log-normal distribution.

There may be slight numerical differences between results generated on the GPU with generators created with curandCreateGenerator() and results calculated on the CPU with generators created with curandCreateGeneratorHost(). These differences arise because of differences in results for transcendental functions. In addition, future versions of CURAND may use newer versions of the CUDA math library, so different versions of CURAND may give slightly different numerical values.

Parameters

  • generator – - Generator to use

  • outputPtr – - Pointer to device memory to store CUDA-generated results, or Pointer to host memory to store CPU-generated results

  • n – - Number of doubles to generate

  • mean – - Mean of normal distribution

  • stddev – - Standard deviation of normal distribution

Returns

  • CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch

  • CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason

  • CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is not a multiple of the quasirandom dimension, or is not a multiple of two for pseudorandom generators

  • CURAND_STATUS_DOUBLE_PRECISION_REQUIRED if the GPU does not support double precision

  • CURAND_STATUS_SUCCESS if the results were generated successfully

curandStatus_t curandGenerateLongLong(curandGenerator_t generator, unsigned long long *outputPtr, size_t num)

Generate 64-bit quasirandom numbers.

Use generator to generate num 64-bit results into the device memory at outputPtr. The device memory must have been previously allocated and be large enough to hold all the results. Launches are done with the stream set using curandSetStream(), or the null stream if no stream has been set.

Results are 64-bit values with every bit random.

Parameters

  • generator – - Generator to use

  • outputPtr – - Pointer to device memory to store CUDA-generated results, or Pointer to host memory to store CPU-generated results

  • num – - Number of random 64-bit values to generate

Returns

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch

  • CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is not a multiple of the quasirandom dimension

  • CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason

  • CURAND_STATUS_TYPE_ERROR if the generator is not a 64 bit quasirandom generator

  • CURAND_STATUS_SUCCESS if the results were generated successfully

curandStatus_t curandGenerateNormal(curandGenerator_t generator, float *outputPtr, size_t n, float mean, float stddev)

Generate normally distributed doubles.

Use generator to generate n float results into the device memory at outputPtr. The device memory must have been previously allocated and be large enough to hold all the results. Launches are done with the stream set using curandSetStream(), or the null stream if no stream has been set.

Results are 32-bit floating point values with mean mean and standard deviation stddev.

Normally distributed results are generated from pseudorandom generators with a Box-Muller transform, and so require n to be even. Quasirandom generators use an inverse cumulative distribution function to preserve dimensionality.

There may be slight numerical differences between results generated on the GPU with generators created with curandCreateGenerator() and results calculated on the CPU with generators created with curandCreateGeneratorHost(). These differences arise because of differences in results for transcendental functions. In addition, future versions of CURAND may use newer versions of the CUDA math library, so different versions of CURAND may give slightly different numerical values.

Parameters

  • generator – - Generator to use

  • outputPtr – - Pointer to device memory to store CUDA-generated results, or Pointer to host memory to store CPU-generated results

  • n – - Number of floats to generate

  • mean – - Mean of normal distribution

  • stddev – - Standard deviation of normal distribution

Returns

  • CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch

  • CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason

  • CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is not a multiple of the quasirandom dimension, or is not a multiple of two for pseudorandom generators

  • CURAND_STATUS_SUCCESS if the results were generated successfully

curandStatus_t curandGenerateNormalDouble(curandGenerator_t generator, double *outputPtr, size_t n, double mean, double stddev)

Generate normally distributed doubles.

Use generator to generate n double results into the device memory at outputPtr. The device memory must have been previously allocated and be large enough to hold all the results. Launches are done with the stream set using curandSetStream(), or the null stream if no stream has been set.

Results are 64-bit floating point values with mean mean and standard deviation stddev.

Normally distributed results are generated from pseudorandom generators with a Box-Muller transform, and so require n to be even. Quasirandom generators use an inverse cumulative distribution function to preserve dimensionality.

There may be slight numerical differences between results generated on the GPU with generators created with curandCreateGenerator() and results calculated on the CPU with generators created with curandCreateGeneratorHost(). These differences arise because of differences in results for transcendental functions. In addition, future versions of CURAND may use newer versions of the CUDA math library, so different versions of CURAND may give slightly different numerical values.

Parameters

  • generator – - Generator to use

  • outputPtr – - Pointer to device memory to store CUDA-generated results, or Pointer to host memory to store CPU-generated results

  • n – - Number of doubles to generate

  • mean – - Mean of normal distribution

  • stddev – - Standard deviation of normal distribution

Returns

  • CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch

  • CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason

  • CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is not a multiple of the quasirandom dimension, or is not a multiple of two for pseudorandom generators

  • CURAND_STATUS_DOUBLE_PRECISION_REQUIRED if the GPU does not support double precision

  • CURAND_STATUS_SUCCESS if the results were generated successfully

curandStatus_t curandGeneratePoisson(curandGenerator_t generator, unsigned int *outputPtr, size_t n, double lambda)

Generate Poisson-distributed unsigned ints.

Use generator to generate n unsigned int results into device memory at outputPtr. The device memory must have been previously allocated and must be large enough to hold all the results. Launches are done with the stream set using curandSetStream(), or the null stream if no stream has been set.

Results are 32-bit unsigned int point values with Poisson distribution, with lambda lambda.

Parameters

  • generator – - Generator to use

  • outputPtr – - Pointer to device memory to store CUDA-generated results, or Pointer to host memory to store CPU-generated results

  • n – - Number of unsigned ints to generate

  • lambda – - lambda for the Poisson distribution

Returns

  • CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch

  • CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason

  • CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is not a multiple of the quasirandom dimension

  • CURAND_STATUS_DOUBLE_PRECISION_REQUIRED if the GPU or sm does not support double precision

  • CURAND_STATUS_OUT_OF_RANGE if lambda is non-positive or greater than 400,000

  • CURAND_STATUS_SUCCESS if the results were generated successfully

curandStatus_t curandGeneratePoissonMethod(curandGenerator_t generator, unsigned int *outputPtr, size_t n, double lambda, curandMethod_t method)
curandStatus_t curandGenerateSeeds(curandGenerator_t generator)

Setup starting states.

Generate the starting state of the generator. This function is automatically called by generation functions such as curandGenerate() and curandGenerateUniform(). It can be called manually for performance testing reasons to separate timings for starting state generation and random number generation.

Parameters

generator – - Generator to update

Returns

  • CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch

  • CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason

  • CURAND_STATUS_SUCCESS if the seeds were generated successfully

curandStatus_t curandGenerateUniform(curandGenerator_t generator, float *outputPtr, size_t num)

Generate uniformly distributed floats.

Use generator to generate num float results into the device memory at outputPtr. The device memory must have been previously allocated and be large enough to hold all the results. Launches are done with the stream set using curandSetStream(), or the null stream if no stream has been set.

Results are 32-bit floating point values between 0.0f and 1.0f, excluding 0.0f and including 1.0f.

Parameters

  • generator – - Generator to use

  • outputPtr – - Pointer to device memory to store CUDA-generated results, or Pointer to host memory to store CPU-generated results

  • num – - Number of floats to generate

Returns

  • CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch

  • CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason

  • CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is not a multiple of the quasirandom dimension

  • CURAND_STATUS_SUCCESS if the results were generated successfully

curandStatus_t curandGenerateUniformDouble(curandGenerator_t generator, double *outputPtr, size_t num)

Generate uniformly distributed doubles.

Use generator to generate num double results into the device memory at outputPtr. The device memory must have been previously allocated and be large enough to hold all the results. Launches are done with the stream set using curandSetStream(), or the null stream if no stream has been set.

Results are 64-bit double precision floating point values between 0.0 and 1.0, excluding 0.0 and including 1.0.

Parameters

  • generator – - Generator to use

  • outputPtr – - Pointer to device memory to store CUDA-generated results, or Pointer to host memory to store CPU-generated results

  • num – - Number of doubles to generate

Returns

  • CURAND_STATUS_ALLOCATION_FAILED if memory could not be allocated

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch

  • CURAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason

  • CURAND_STATUS_LENGTH_NOT_MULTIPLE if the number of output samples is not a multiple of the quasirandom dimension

  • CURAND_STATUS_DOUBLE_PRECISION_REQUIRED if the GPU does not support double precision

  • CURAND_STATUS_SUCCESS if the results were generated successfully

curandStatus_t curandGetDirectionVectors32(curandDirectionVectors32_t *vectors[], curandDirectionVectorSet_t set)

Get direction vectors for 32-bit quasirandom number generation.

Get a pointer to an array of direction vectors that can be used for quasirandom number generation. The resulting pointer will reference an array of direction vectors in host memory.

The array contains vectors for many dimensions. Each dimension has 32 vectors. Each individual vector is an unsigned int.

Legal values for set are:

  • CURAND_DIRECTION_VECTORS_32_JOEKUO6 (20,000 dimensions)

  • CURAND_SCRAMBLED_DIRECTION_VECTORS_32_JOEKUO6 (20,000 dimensions)

Parameters

  • vectors – - Address of pointer in which to return direction vectors

  • set – - Which set of direction vectors to use

Returns

  • CURAND_STATUS_OUT_OF_RANGE if the choice of set is invalid

  • CURAND_STATUS_SUCCESS if the pointer was set successfully

curandStatus_t curandGetDirectionVectors64(curandDirectionVectors64_t *vectors[], curandDirectionVectorSet_t set)

Get direction vectors for 64-bit quasirandom number generation.

Get a pointer to an array of direction vectors that can be used for quasirandom number generation. The resulting pointer will reference an array of direction vectors in host memory.

The array contains vectors for many dimensions. Each dimension has 64 vectors. Each individual vector is an unsigned long long.

Legal values for set are:

  • CURAND_DIRECTION_VECTORS_64_JOEKUO6 (20,000 dimensions)

  • CURAND_SCRAMBLED_DIRECTION_VECTORS_64_JOEKUO6 (20,000 dimensions)

Parameters

  • vectors – - Address of pointer in which to return direction vectors

  • set – - Which set of direction vectors to use

Returns

  • CURAND_STATUS_OUT_OF_RANGE if the choice of set is invalid

  • CURAND_STATUS_SUCCESS if the pointer was set successfully

curandStatus_t curandGetProperty(libraryPropertyType type, int *value)

Return the value of the curand property.

Return in *value the number for the property described by type of the dynamically linked CURAND library.

Parameters

  • type – - CUDA library property

  • value – - integer value for the requested property

Returns

  • CURAND_STATUS_SUCCESS if the property value was successfully returned

  • CURAND_STATUS_OUT_OF_RANGE if the property type is not recognized

curandStatus_t curandGetScrambleConstants32(unsigned int **constants)

Get scramble constants for 32-bit scrambled Sobol’ .

Get a pointer to an array of scramble constants that can be used for quasirandom number generation. The resulting pointer will reference an array of unsinged ints in host memory.

The array contains constants for many dimensions. Each dimension has a single unsigned int constant.

Parameters

constants – - Address of pointer in which to return scramble constants

Returns

  • CURAND_STATUS_SUCCESS if the pointer was set successfully

curandStatus_t curandGetScrambleConstants64(unsigned long long **constants)

Get scramble constants for 64-bit scrambled Sobol’ .

Get a pointer to an array of scramble constants that can be used for quasirandom number generation. The resulting pointer will reference an array of unsinged long longs in host memory.

The array contains constants for many dimensions. Each dimension has a single unsigned long long constant.

Parameters

constants – - Address of pointer in which to return scramble constants

Returns

  • CURAND_STATUS_SUCCESS if the pointer was set successfully

curandStatus_t curandGetVersion(int *version)

Return the version number of the library.

Return in *version the version number of the dynamically linked CURAND library. The format is the same as CUDART_VERSION from the CUDA Runtime. The only supported configuration is CURAND version equal to CUDA Runtime version.

Parameters

version – - CURAND library version

Returns

  • CURAND_STATUS_SUCCESS if the version number was successfully returned

curandStatus_t curandSetGeneratorOffset(curandGenerator_t generator, unsigned long long offset)

Set the absolute offset of the pseudo or quasirandom number generator.

Set the absolute offset of the pseudo or quasirandom number generator.

All values of offset are valid. The offset position is absolute, not relative to the current position in the sequence.

Parameters

  • generator – - Generator to modify

  • offset – - Absolute offset position

Returns

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_SUCCESS if generator offset was set successfully

curandStatus_t curandSetGeneratorOrdering(curandGenerator_t generator, curandOrdering_t order)

Set the ordering of results of the pseudo or quasirandom number generator.

Set the ordering of results of the pseudo or quasirandom number generator.

Legal values of order for pseudorandom generators are:

  • CURAND_ORDERING_PSEUDO_DEFAULT

  • CURAND_ORDERING_PSEUDO_BEST

  • CURAND_ORDERING_PSEUDO_SEEDED

  • CURAND_ORDERING_PSEUDO_LEGACY

Legal values of order for quasirandom generators are:

  • CURAND_ORDERING_QUASI_DEFAULT

Parameters

  • generator – - Generator to modify

  • order – - Ordering of results

Returns

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_OUT_OF_RANGE if the ordering is not valid

  • CURAND_STATUS_SUCCESS if generator ordering was set successfully

curandStatus_t curandSetPseudoRandomGeneratorSeed(curandGenerator_t generator, unsigned long long seed)

Set the seed value of the pseudo-random number generator.

Set the seed value of the pseudorandom number generator. All values of seed are valid. Different seeds will produce different sequences. Different seeds will often not be statistically correlated with each other, but some pairs of seed values may generate sequences which are statistically correlated.

Parameters

  • generator – - Generator to modify

  • seed – - Seed value

Returns

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_TYPE_ERROR if the generator is not a pseudorandom number generator

  • CURAND_STATUS_SUCCESS if generator seed was set successfully

curandStatus_t curandSetQuasiRandomGeneratorDimensions(curandGenerator_t generator, unsigned int num_dimensions)

Set the number of dimensions.

Set the number of dimensions to be generated by the quasirandom number generator.

Legal values for num_dimensions are 1 to 20000.

Parameters

  • generator – - Generator to modify

  • num_dimensions – - Number of dimensions

Returns

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_OUT_OF_RANGE if num_dimensions is not valid

  • CURAND_STATUS_TYPE_ERROR if the generator is not a quasirandom number generator

  • CURAND_STATUS_SUCCESS if generator ordering was set successfully

curandStatus_t curandSetStream(curandGenerator_t generator, cudaStream_t stream)

Set the current stream for CURAND kernel launches.

Set the current stream for CURAND kernel launches. All library functions will use this stream until set again.

Parameters

  • generator – - Generator to modify

  • stream – - Stream to use or ::NULL for null stream

Returns

  • CURAND_STATUS_NOT_INITIALIZED if the generator was never created

  • CURAND_STATUS_SUCCESS if stream was set successfully