C/C++ API Reference#
This chapter describes the hipRAND C and C++ API.
Device Functions#
- group hipranddevice
Defines
-
HIPRAND_PHILOX4x32_DEFAULT_SEED#
Default seed for PHILOX4x32 PRNG.
-
HIPRAND_XORWOW_DEFAULT_SEED#
Default seed for XORWOW PRNG.
-
HIPRAND_MRG32K3A_DEFAULT_SEED#
Default seed for MRG32K3A PRNG.
-
HIPRAND_MTGP32_DEFAULT_SEED#
Default seed for MTGP32 PRNG.
-
HIPRAND_MT19937_DEFAULT_SEED#
Default seed for MT19937 PRNG.
Functions
-
void hiprand_mtgp32_block_copy(hiprandStateMtgp32_t *src, hiprandStateMtgp32_t *dest)#
Copy MTGP32 state to another state using block of threads.
Copies a MTGP32 state
src
todest
using a block of threads efficiently. Example usage would be:__global__ void generate_kernel(hiprandStateMtgp32_t * states, unsigned int * output, const size_t size) { const unsigned int state_id = hipBlockIdx_x; unsigned int index = hipBlockIdx_x * hipBlockDim_x + hipThreadIdx_x; unsigned int stride = hipGridDim_x * hipBlockDim_x; __shared__ GeneratorState state; hiprand_mtgp32_block_copy(&states[state_id], &state); while(index < size) { output[index] = rocrand(&state); index += stride; } hiprand_mtgp32_block_copy(&state, &states[state_id]); }
- Parameters:
src – - Pointer to a state to copy from
dest – - Pointer to a state to copy to
-
void hiprand_mtgp32_set_params(hiprandStateMtgp32_t *state, mtgp32_kernel_params_t *params)#
Changes parameters of a MTGP32 state.
- Parameters:
state – - Pointer to a MTGP32 state
params – - Pointer to new parameters
-
template<class StateType>
void hiprand_init(const unsigned long long seed, const unsigned long long subsequence, const unsigned long long offset, StateType *state)# Initializes a PRNG state.
See also: hiprandMakeMTGP32KernelState()
- Template Parameters:
StateType – - Pseudorandom number generator state type.
StateType
type must be one of following types:hiprandStateXORWOW_t
,hiprandStatePhilox4_32_10_t
, orhiprandStateMRG32k3a_t
- Parameters:
seed – - Pseudorandom number generator’s seed
subsequence – - Number of subsequence to skipahead
offset – - Absolute subsequence offset, i.e. how many states from current subsequence should be skipped
state – - Pointer to a state to initialize
-
void hiprand_init(hiprandDirectionVectors32_t direction_vectors, unsigned int offset, hiprandStateSobol32_t *state)#
Initializes a Sobol32 state.
- Parameters:
direction_vectors – - Pointer to array of 32
unsigned int
s that represent the direction numbers.offset – - Absolute subsequence offset, i.e. how many states should be skipped.
state – - Pointer to a state to initialize.
-
void hiprand_init(hiprandDirectionVectors32_t direction_vectors, unsigned int scramble_constant, unsigned int offset, hiprandStateScrambledSobol32_t *state)#
Initializes a ScrambledSobol32 state.
- Parameters:
direction_vectors – - Pointer to array of 32
unsigned int
s that represent the direction numbers.scramble_constant – - Constant used for scrambling the sequence.
offset – - Absolute subsequence offset, i.e. how many states should be skipped.
state – - Pointer to a state to initialize.
-
void hiprand_init(hiprandDirectionVectors64_t direction_vectors, unsigned int offset, hiprandStateSobol64_t *state)#
Initializes a Sobol64 state.
- Parameters:
direction_vectors – - Pointer to array of 64
unsigned long long int
s that represent the direction numbers.offset – - Absolute subsequence offset, i.e. how many states should be skipped.
state – - Pointer to a state to initialize.
-
void hiprand_init(hiprandDirectionVectors64_t direction_vectors, unsigned long long int scramble_constant, unsigned int offset, hiprandStateScrambledSobol64_t *state)#
Initializes a ScrambledSobol64 state.
- Parameters:
direction_vectors – - Pointer to array of 64
unsigned long long int
s that represent the direction numbers.scramble_constant – - Constant used for scrambling the sequence.
offset – - Absolute subsequence offset, i.e. how many states should be skipped.
state – - Pointer to a state to initialize.
-
template<class StateType>
void skipahead(unsigned long long n, StateType *state)# Updates RNG state skipping
n
states ahead.- Template Parameters:
StateType – - Random number generator state type.
StateType
type must be one of following types:hiprandStateXORWOW_t
,hiprandStatePhilox4_32_10_t
,hiprandStateMRG32k3a_t
,hiprandStateSobol32_t
,hiprandStateScrambledSobol32_t
,hiprandStateSobol64_t
, orhiprandStateScrambledSobol64_t
.- Parameters:
n – - Number of states to skipahead
state – - Pointer to a state to modify
-
template<class StateType>
void skipahead_sequence(unsigned long long n, StateType *state)# Updates PRNG state skipping
n
sequences ahead.———-— | ———-— XORWOW | 2^67 Philox | 4 * 2^64 MRG32k3a | 2^67PRNG | Sequence size [Number of elements]
- Template Parameters:
StateType – - Random number generator state type.
StateType
type must be one of following types:hiprandStateXORWOW_t
,hiprandStatePhilox4_32_10_t
, orhiprandStateMRG32k3a_t
- Parameters:
n – - Number of subsequences to skipahead
state – - Pointer to a state to update
-
template<class StateType>
void skipahead_subsequence(unsigned long long n, StateType *state)# Updates PRNG state skipping
n
subsequences ahead.———-— | ———-— XORWOW | 2^67 Philox | 4 * 2^64 MRG32k3a | 2^127PRNG | Subsequence size [Number of elements]
- Template Parameters:
StateType – - Random number generator state type.
StateType
type must be one of following types:hiprandStateXORWOW_t
,hiprandStatePhilox4_32_10_t
, orhiprandStateMRG32k3a_t
- Parameters:
n – - Number of subsequences to skipahead
state – - Pointer to a state to update
-
template<class StateType>
unsigned int hiprand(StateType *state)# Generates uniformly distributed random
unsigned int
from [0; 2^32 - 1] range.- Template Parameters:
StateType – - Random number generator state type.
StateType
type must be one of following types:hiprandStateXORWOW_t
,hiprandStatePhilox4_32_10_t
,hiprandStateMRG32k3a_t
,hiprandStateMtgp32_t
,hiprandStateSobol32_t
,hiprandStateScrambledSobol32_t
.- Parameters:
state – - Pointer to a RNG state to use
- Returns:
Uniformly distributed random 32-bit
unsigned int
-
uint4 hiprand4(hiprandStatePhilox4_32_10_t *state)#
Generates four uniformly distributed random
unsigned int
s from [0; 2^32 - 1] range.- Parameters:
state – - Pointer to a Philox state to use
- Returns:
Four uniformly distributed random 32-bit
unsigned int
s asuint4
-
template<class StateType>
unsigned long long int hiprand_long_long(StateType *state)# Generates uniformly distributed random
unsigned long long int
from [0; 2^64 - 1] range.- Template Parameters:
StateType – - Random number generator state type.
StateType
type must be one of the following types:hiprandStateSobol64_t
orhiprandStateScrambledSobol64_t
.- Parameters:
state – - Pointer to a RNG state to use
- Returns:
Uniformly distributed random 64-bit
unsigned long long int
-
template<class StateType>
float hiprand_uniform(StateType *state)# Generates uniformly distributed random
float
value from (0; 1] range.- Template Parameters:
StateType – - Random number generator state type.
- Parameters:
state – - Pointer to a RNG state to use
- Returns:
Uniformly distributed random
float
value
-
float4 hiprand_uniform4(hiprandStatePhilox4_32_10_t *state)#
Generates four uniformly distributed random
float
value from (0; 1] range.- Parameters:
state – - Pointer to a Philox state to use
- Returns:
Four uniformly distributed random
float
values asfloat4
-
template<class StateType>
double hiprand_uniform_double(StateType *state)# Generates uniformly distributed random
double
value from (0; 1] range.Note: When
state
is of type:hiprandStateMRG32k3a_t
,hiprandStateMtgp32_t
,hiprandStateSobol32_t
, orhiprandStateScrambledSobol32_t
then the returneddouble
value is generated using only 32 random bits (oneunsigned int
value). In case of the Sobol types, this is done to guarantee the quasirandom properties.- Template Parameters:
StateType – - Random number generator state type.
- Parameters:
state – - Pointer to a RNG state to use
- Returns:
Uniformly distributed random
double
value
-
double2 hiprand_uniform2_double(hiprandStatePhilox4_32_10_t *state)#
Generates two uniformly distributed random
double
values from (0; 1] range.- Parameters:
state – - Pointer to a Philox state to use
- Returns:
Two uniformly distributed random
double
values asdouble2
-
double4 hiprand_uniform4_double(hiprandStatePhilox4_32_10_t *state)#
Generates four uniformly distributed random
double
values from (0; 1] range.- Parameters:
state – - Pointer to a Philox state to use
- Returns:
Four uniformly distributed random
double
values asdouble4
-
template<class StateType>
float hiprand_normal(StateType *state)# Generates normally distributed random
float
value.Mean value of normal distribution is equal to 0.0, and standard deviation equals 1.0.
- Template Parameters:
StateType – - Random number generator state type.
- Parameters:
state – - Pointer to a RNG state to use
- Returns:
Normally distributed random
float
value
-
template<class StateType>
float2 hiprand_normal2(StateType *state)# Generates two normally distributed random
float
values.Mean value of normal distribution is equal to 0.0, and standard deviation equals 1.0.
- Template Parameters:
StateType – - Random number generator state type.
StateType
type must be one of following types:hiprandStateXORWOW_t
,hiprandStatePhilox4_32_10_t
, orhiprandStateMRG32k3a_t
- Parameters:
state – - Pointer to a RNG state to use
- Returns:
Two normally distributed random
float
values asfloat2
-
float4 hiprand_normal4(hiprandStatePhilox4_32_10_t *state)#
Generates four normally distributed random
float
values.Mean value of normal distribution is equal to 0.0, and standard deviation equals 1.0.
- Parameters:
state – - Pointer to a Philox state to use
- Returns:
Four normally distributed random
float
values asfloat4
-
template<class StateType>
double hiprand_normal_double(StateType *state)# Generates normally distributed random
double
value.Mean value of normal distribution is equal to 0.0, and standard deviation equals 1.0.
- Template Parameters:
StateType – - Random number generator state type.
- Parameters:
state – - Pointer to a RNG state to use
- Returns:
Normally distributed random
double
value
-
template<class StateType>
double2 hiprand_normal2_double(StateType *state)# Generates two normally distributed random
double
values.Mean value of normal distribution is equal to 0.0, and standard deviation equals 1.0.
- Template Parameters:
StateType – - Random number generator state type.
StateType
type must be one of following types:hiprandStateXORWOW_t
,hiprandStatePhilox4_32_10_t
, orhiprandStateMRG32k3a_t
- Parameters:
state – - Pointer to a RNG state to use
- Returns:
Two normally distributed random
double
values asdouble2
-
double4 hiprand_normal4_double(hiprandStatePhilox4_32_10_t *state)#
Generates four normally distributed random
double
values.Mean value of normal distribution is equal to 0.0, and standard deviation equals 1.0.
- Parameters:
state – - Pointer to a Philox state to use
- Returns:
Four normally distributed random
double
values asdouble4
-
template<class StateType>
float hiprand_log_normal(StateType *state, float mean, float stddev)# Generates log-normally distributed random
float
value.- Template Parameters:
StateType – - Random number generator state type.
- Parameters:
state – - Pointer to a RNG state to use
mean – - Mean value of log-normal distribution
stddev – - Standard deviation value of log-normal distribution
- Returns:
Log-normally distributed random
float
value
-
template<class StateType>
float2 hiprand_log_normal2(StateType *state, float mean, float stddev)# Generates two log-normally distributed random
float
values.- Template Parameters:
StateType – - Random number generator state type.
StateType
type must be one of following types:hiprandStateXORWOW_t
,hiprandStatePhilox4_32_10_t
, orhiprandStateMRG32k3a_t
- Parameters:
state – - Pointer to a RNG state to use
mean – - Mean value of log-normal distribution
stddev – - Standard deviation value of log-normal distribution
- Returns:
Two log-normally distributed random
float
values asfloat2
-
float4 hiprand_log_normal4(hiprandStatePhilox4_32_10_t *state, float mean, float stddev)#
Generates four log-normally distributed random
float
values.- Parameters:
state – - Pointer to a Philox state to use
mean – - Mean value of log-normal distribution
stddev – - Standard deviation value of log-normal distribution
- Returns:
Four log-normally distributed random
float
values asfloat4
-
template<class StateType>
double hiprand_log_normal_double(StateType *state, double mean, double stddev)# Generates log-normally distributed random
double
value.- Template Parameters:
StateType – - Random number generator state type.
- Parameters:
state – - Pointer to a RNG state to use
mean – - Mean value of log-normal distribution
stddev – - Standard deviation value of log-normal distribution
- Returns:
Log-normally distributed random
double
value
-
template<class StateType>
double2 hiprand_log_normal2_double(StateType *state, double mean, double stddev)# Generates two log-normally distributed random
double
values.- Template Parameters:
StateType – - Random number generator state type.
StateType
type must be one of following types:hiprandStateXORWOW_t
,hiprandStatePhilox4_32_10_t
,hiprandStateMRG32k3a_t
, orhiprandStateMtgp32_t
.- Parameters:
state – - Pointer to a RNG state to use
mean – - Mean value of log-normal distribution
stddev – - Standard deviation value of log-normal distribution
- Returns:
Two log-normally distributed random
double
values asdouble2
-
double4 hiprand_log_normal4_double(hiprandStatePhilox4_32_10_t *state, double mean, double stddev)#
Generates four log-normally distributed random
double
values.- Parameters:
state – - Pointer to a Philox state to use
mean – - Mean value of log-normal distribution
stddev – - Standard deviation value of log-normal distribution
- Returns:
Four log-normally distributed random
double
values asdouble4
-
template<class StateType>
uint hiprand_poisson(StateType *state, double lambda)# Generates Poisson-distributed random
unsigned int
value.- Template Parameters:
StateType – - Random number generator state type.
- Parameters:
state – - Pointer to a RNG state to use
lambda – - Lambda (mean) parameter of Poisson distribution
- Returns:
Poisson-distributed random
unsigned int
value
-
uint4 hiprand_poisson4(hiprandStatePhilox4_32_10_t *state, double lambda)#
Generates four Poisson-distributed random
unsigned int
values.- Parameters:
state – - Pointer to a Philox state to use
lambda – - Lambda (mean) parameter of Poisson distribution
- Returns:
Four Poisson-distributed random
unsigned int
values asuint4
-
template<class StateType>
uint hiprand_discrete(StateType *state, hiprandDiscreteDistribution_t discrete_distribution)# Generates random
unsigned int
value according to given discrete distribution.See also: hiprandCreatePoissonDistribution()
- Template Parameters:
StateType – - Random number generator state type.
- Parameters:
state – - Pointer to a RNG state to use
discrete_distribution – - Discrete distribution
- Returns:
Random
unsigned int
value
-
uint4 hiprand_discrete4(hiprandStatePhilox4_32_10_t *state, hiprandDiscreteDistribution_t discrete_distribution)#
Generates four random
unsigned int
values according to given discrete distribution.See also: hiprandCreatePoissonDistribution()
- Parameters:
state – - Pointer to a Philox state to use
discrete_distribution – - Discrete distribution
- Returns:
Four random
unsigned int
values asuint4
- inline __host__ hiprandStatus_t hiprandMakeMTGP32Constants (const mtgp32_params_fast_t params[], mtgp32_kernel_params_t *p)
Loads parameters for MTGP32.
Loads parameters for use by kernel functions on the host-side and copies the results to the specified location in device memory.
- Parameters:
params – - Pointer to an array of type mtgp32_params_fast_t allocated in host memory
p – - Pointer to a mtgp32_kernel_params_t structure allocated in device memory
- Returns:
HIPRAND_STATUS_ALLOCATION_FAILED if parameters could not be loaded
HIPRAND_STATUS_SUCCESS if parameters are loaded
- inline __host__ hiprandStatus_t hiprandMakeMTGP32KernelState (hiprandStateMtgp32_t *s, mtgp32_params_fast_t params[], mtgp32_kernel_params_t *k, int n, unsigned long long seed)
Initializes MTGP32 states.
Initializes MTGP32 states on the host-side by allocating a state array in host memory, initializes that array, and copies the result to device memory.
- Parameters:
s – - Pointer to an array of states in device memory
params – - Pointer to an array of type mtgp32_params_fast_t in host memory
k – - Pointer to a mtgp32_kernel_params_t structure allocated in device memory
n – - Number of states to initialize
seed – - Seed value
- Returns:
HIPRAND_STATUS_ALLOCATION_FAILED if states could not be initialized
HIPRAND_STATUS_SUCCESS if states are initialized
-
HIPRAND_PHILOX4x32_DEFAULT_SEED#
C Host API#
- group hiprandhost
Defines
-
HIPRAND_VERSION#
hipRAND library version
Version number may not be visible in the documentation.
HIPRAND_VERSION % 100
is the patch level,HIPRAND_VERSION / 100 % 1000
is the minor version,HIPRAND_VERSION / 100000
is the major version.For example, if
HIPRAND_VERSION
is100500
, then the major version is1
, the minor version is5
, and the patch level is0
.
-
HIPRAND_DEFAULT_MAX_BLOCK_SIZE#
-
HIPRAND_DEFAULT_MIN_WARPS_PER_EU#
Typedefs
-
typedef enum hiprandStatus hiprandStatus_t#
hipRAND function call status type
-
typedef enum hiprandRngType hiprandRngType_t#
hipRAND generator type
Enums
-
enum hiprandStatus#
hipRAND function call status type
Values:
-
enumerator HIPRAND_STATUS_SUCCESS#
Success.
-
enumerator HIPRAND_STATUS_VERSION_MISMATCH#
Header file and linked library version do not match.
-
enumerator HIPRAND_STATUS_NOT_INITIALIZED#
Generator not created.
-
enumerator HIPRAND_STATUS_ALLOCATION_FAILED#
Memory allocation failed.
-
enumerator HIPRAND_STATUS_TYPE_ERROR#
Generator type is wrong.
-
enumerator HIPRAND_STATUS_OUT_OF_RANGE#
Argument out of range.
-
enumerator HIPRAND_STATUS_LENGTH_NOT_MULTIPLE#
Requested size is not a multiple of quasirandom generator’s dimension, or requested size is not even (see hiprandGenerateNormal()), or pointer is misaligned (see hiprandGenerateNormal())
-
enumerator HIPRAND_STATUS_DOUBLE_PRECISION_REQUIRED#
GPU does not have double precision.
-
enumerator HIPRAND_STATUS_LAUNCH_FAILURE#
Kernel launch failure.
-
enumerator HIPRAND_STATUS_PREEXISTING_FAILURE#
Preexisting failure on library entry.
-
enumerator HIPRAND_STATUS_INITIALIZATION_FAILED#
Initialization of HIP failed.
-
enumerator HIPRAND_STATUS_ARCH_MISMATCH#
Architecture mismatch, GPU does not support requested feature.
-
enumerator HIPRAND_STATUS_INTERNAL_ERROR#
Internal library error.
-
enumerator HIPRAND_STATUS_NOT_IMPLEMENTED#
Feature not implemented yet.
-
enumerator HIPRAND_STATUS_SUCCESS#
-
enum hiprandRngType#
hipRAND generator type
Values:
-
enumerator HIPRAND_RNG_PSEUDO_DEFAULT#
Default pseudorandom generator.
-
enumerator HIPRAND_RNG_PSEUDO_XORWOW#
XORWOW pseudorandom generator.
-
enumerator HIPRAND_RNG_PSEUDO_MRG32K3A#
MRG32k3a pseudorandom generator.
-
enumerator HIPRAND_RNG_PSEUDO_MTGP32#
Mersenne Twister MTGP32 pseudorandom generator.
-
enumerator HIPRAND_RNG_PSEUDO_MT19937#
Mersenne Twister 19937.
-
enumerator HIPRAND_RNG_PSEUDO_PHILOX4_32_10#
PHILOX_4x32 (10 rounds) pseudorandom generator.
-
enumerator HIPRAND_RNG_QUASI_DEFAULT#
Default quasirandom generator.
-
enumerator HIPRAND_RNG_QUASI_SOBOL32#
Sobol32 quasirandom generator.
-
enumerator HIPRAND_RNG_QUASI_SCRAMBLED_SOBOL32#
Scrambled Sobol32 quasirandom generator.
-
enumerator HIPRAND_RNG_QUASI_SOBOL64#
Sobol64 quasirandom generator.
-
enumerator HIPRAND_RNG_QUASI_SCRAMBLED_SOBOL64#
Scrambled Sobol64 quasirandom generator.
-
enumerator HIPRAND_RNG_PSEUDO_DEFAULT#
Functions
-
hiprandStatus_t hiprandCreateGenerator(hiprandGenerator_t *generator, hiprandRngType_t rng_type)#
Creates a new random number generator.
Creates a new random number generator of type
rng_type
, and returns it ingenerator
. That generator will use GPU to create random numbers.Values for
rng_type
are:HIPRAND_RNG_PSEUDO_DEFAULT
HIPRAND_RNG_PSEUDO_XORWOW
HIPRAND_RNG_PSEUDO_MRG32K3A
HIPRAND_RNG_PSEUDO_MTGP32
HIPRAND_RNG_PSEUDO_MT19937
HIPRAND_RNG_PSEUDO_PHILOX4_32_10
HIPRAND_RNG_QUASI_DEFAULT
HIPRAND_RNG_QUASI_SOBOL32
HIPRAND_RNG_QUASI_SCRAMBLED_SOBOL32
HIPRAND_RNG_QUASI_SOBOL64
HIPRAND_RNG_QUASI_SCRAMBLED_SOBOL64
- Parameters:
generator – - Pointer to generator
rng_type – - Type of random number generator to create
- Returns:
HIPRAND_STATUS_ALLOCATION_FAILED, if memory allocation failed
HIPRAND_STATUS_INITIALIZATION_FAILED if there was a problem setting up the GPU
HIPRAND_STATUS_VERSION_MISMATCH if the header file version does not match the dynamically linked library version
HIPRAND_STATUS_TYPE_ERROR if the value for
rng_type
is invalidHIPRAND_STATUS_NOT_IMPLEMENTED if generator of type
rng_type
is not implemented yetHIPRAND_STATUS_SUCCESS if generator was created successfully
-
hiprandStatus_t hiprandCreateGeneratorHost(hiprandGenerator_t *generator, hiprandRngType_t rng_type)#
Creates a new random number generator on host.
Creates a new host random number generator of type
rng_type
and returns it ingenerator
. Created generator will use host CPU to generate random numbers.Values for
rng_type
are:HIPRAND_RNG_PSEUDO_DEFAULT
HIPRAND_RNG_PSEUDO_XORWOW
HIPRAND_RNG_PSEUDO_MRG32K3A
HIPRAND_RNG_PSEUDO_MTGP32
HIPRAND_RNG_PSEUDO_MT19937
HIPRAND_RNG_PSEUDO_PHILOX4_32_10
HIPRAND_RNG_QUASI_DEFAULT
HIPRAND_RNG_QUASI_SOBOL32
HIPRAND_RNG_QUASI_SCRAMBLED_SOBOL32
HIPRAND_RNG_QUASI_SOBOL64
HIPRAND_RNG_QUASI_SCRAMBLED_SOBOL64
- Parameters:
generator – - Pointer to generator
rng_type – - Type of random number generator to create
- Returns:
HIPRAND_STATUS_ALLOCATION_FAILED, if memory allocation failed
HIPRAND_STATUS_VERSION_MISMATCH if the header file version does not match the dynamically linked library version
HIPRAND_STATUS_TYPE_ERROR if the value for
rng_type
is invalidHIPRAND_STATUS_NOT_IMPLEMENTED if host generator of type
rng_type
is not implemented yetHIPRAND_STATUS_SUCCESS if generator was created successfully
-
hiprandStatus_t hiprandDestroyGenerator(hiprandGenerator_t generator)#
Destroys random number generator.
Destroys random number generator and frees related memory.
- Parameters:
generator – - Generator to be destroyed
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_SUCCESS if generator was destroyed successfully
-
hiprandStatus_t hiprandGenerate(hiprandGenerator_t generator, unsigned int *output_data, size_t n)#
Generates uniformly distributed 32-bit unsigned integers.
Generates
n
uniformly distributed 32-bit unsigned integers and saves them tooutput_data
.Generated numbers are between
0
and2^32
, including0
and excluding2^32
.Note:
generator
must be not be of typeHIPRAND_RNG_QUASI_SOBOL64
orHIPRAND_RNG_QUASI_SCRAMBLED_SOBOL64
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of 32-bit unsigned integers to generate
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateChar(hiprandGenerator_t generator, unsigned char *output_data, size_t n)#
Generates uniformly distributed 8-bit unsigned integers.
Generates
n
uniformly distributed 8-bit unsigned integers and saves them tooutput_data
.Generated numbers are between
0
and2^8
, including0
and excluding2^8
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of 8-bit unsigned integers to generate
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateShort(hiprandGenerator_t generator, unsigned short *output_data, size_t n)#
Generates uniformly distributed 16-bit unsigned integers.
Generates
n
uniformly distributed 16-bit unsigned integers and saves them tooutput_data
.Generated numbers are between
0
and2^16
, including0
and excluding2^16
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of 16-bit unsigned integers to generate
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateLongLong(hiprandGenerator_t generator, unsigned long long *output_data, size_t n)#
Generates uniformly distributed 64-bit unsigned integers.
Generates
n
uniformly distributed 64-bit unsigned integers and saves them tooutput_data
.Generated numbers are between
0
and2^64
, including0
and excluding2^64
.Note:
generator
must be of typeHIPRAND_RNG_QUASI_SOBOL64
orHIPRAND_RNG_QUASI_SCRAMBLED_SOBOL64
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of 64-bit unsigned integers to generate
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateUniform(hiprandGenerator_t generator, float *output_data, size_t n)#
Generates uniformly distributed floats.
Generates
n
uniformly distributed 32-bit floating-point values and saves them tooutput_data
.Generated numbers are between
0.0f
and1.0f
, excluding0.0f
and including1.0f
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of floats to generate
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_LENGTH_NOT_MULTIPLE if
n
is not a multiple of the dimension of used quasi-random generatorHIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateUniformDouble(hiprandGenerator_t generator, double *output_data, size_t n)#
Generates uniformly distributed double-precision floating-point values.
Generates
n
uniformly distributed 64-bit double-precision floating-point values and saves them tooutput_data
.Generated numbers are between
0.0
and1.0
, excluding0.0
and including1.0
.Note: When
generator
is of type:HIPRAND_RNG_PSEUDO_MRG32K3A
,HIPRAND_RNG_PSEUDO_MTGP32
,HIPRAND_RNG_QUASI_SOBOL32
, orHIPRAND_RNG_QUASI_SCRAMBLED_SOBOL32
then the returneddouble
values are generated from only 32 random bits each (oneunsigned int
value per one generateddouble
).- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of floats to generate
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_LENGTH_NOT_MULTIPLE if
n
is not a multiple of the dimension of used quasi-random generatorHIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateUniformHalf(hiprandGenerator_t generator, half *output_data, size_t n)#
Generates uniformly distributed half-precision floating-point values.
Generates
n
uniformly distributed 16-bit half-precision floating-point values and saves them tooutput_data
.Generated numbers are between
0.0
and1.0
, excluding0.0
and including1.0
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of halfs to generate
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_LENGTH_NOT_MULTIPLE if
n
is not a multiple of the dimension of used quasi-random generatorHIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateNormal(hiprandGenerator_t generator, float *output_data, size_t n, float mean, float stddev)#
Generates normally distributed floats.
Generates
n
normally distributed 32-bit floating-point values and saves them tooutput_data
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of floats to generate
mean – - Mean value of normal distribution
stddev – - Standard deviation value of normal distribution
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_LENGTH_NOT_MULTIPLE if
n
is not even,output_data
is not aligned tosizeof(float2)
bytes, orn
is not a multiple of the dimension of used quasi-random generatorHIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateNormalDouble(hiprandGenerator_t generator, double *output_data, size_t n, double mean, double stddev)#
Generates normally distributed doubles.
Generates
n
normally distributed 64-bit double-precision floating-point numbers and saves them tooutput_data
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of doubles to generate
mean – - Mean value of normal distribution
stddev – - Standard deviation value of normal distribution
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_LENGTH_NOT_MULTIPLE if
n
is not even,output_data
is not aligned tosizeof(double2)
bytes, orn
is not a multiple of the dimension of used quasi-random generatorHIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateNormalHalf(hiprandGenerator_t generator, half *output_data, size_t n, half mean, half stddev)#
Generates normally distributed halfs.
Generates
n
normally distributed 16-bit half-precision floating-point numbers and saves them tooutput_data
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of halfs to generate
mean – - Mean value of normal distribution
stddev – - Standard deviation value of normal distribution
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_LENGTH_NOT_MULTIPLE if
n
is not even,output_data
is not aligned tosizeof(half2)
bytes, orn
is not a multiple of the dimension of used quasi-random generatorHIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateLogNormal(hiprandGenerator_t generator, float *output_data, size_t n, float mean, float stddev)#
Generates log-normally distributed floats.
Generates
n
log-normally distributed 32-bit floating-point values and saves them tooutput_data
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of floats to generate
mean – - Mean value of log normal distribution
stddev – - Standard deviation value of log normal distribution
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_LENGTH_NOT_MULTIPLE if
n
is not even,output_data
is not aligned tosizeof(float2)
bytes, orn
is not a multiple of the dimension of used quasi-random generatorHIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateLogNormalDouble(hiprandGenerator_t generator, double *output_data, size_t n, double mean, double stddev)#
Generates log-normally distributed doubles.
Generates
n
log-normally distributed 64-bit double-precision floating-point values and saves them tooutput_data
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of doubles to generate
mean – - Mean value of log normal distribution
stddev – - Standard deviation value of log normal distribution
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_LENGTH_NOT_MULTIPLE if
n
is not even,output_data
is not aligned tosizeof(double2)
bytes, orn
is not a multiple of the dimension of used quasi-random generatorHIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateLogNormalHalf(hiprandGenerator_t generator, half *output_data, size_t n, half mean, half stddev)#
Generates log-normally distributed halfs.
Generates
n
log-normally distributed 16-bit half-precision floating-point values and saves them tooutput_data
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of halfs to generate
mean – - Mean value of log normal distribution
stddev – - Standard deviation value of log normal distribution
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_LENGTH_NOT_MULTIPLE if
n
is not even,output_data
is not aligned tosizeof(half2)
bytes, orn
is not a multiple of the dimension of used quasi-random generatorHIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGeneratePoisson(hiprandGenerator_t generator, unsigned int *output_data, size_t n, double lambda)#
Generates Poisson-distributed 32-bit unsigned integers.
Generates
n
Poisson-distributed 32-bit unsigned integers and saves them tooutput_data
.- Parameters:
generator – - Generator to use
output_data – - Pointer to memory to store generated numbers
n – - Number of 32-bit unsigned integers to generate
lambda – - lambda for the Poisson distribution
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_LAUNCH_FAILURE if generator failed to launch kernel
HIPRAND_STATUS_OUT_OF_RANGE if lambda is non-positive
HIPRAND_STATUS_LENGTH_NOT_MULTIPLE if
n
is not a multiple of the dimension of used quasi-random generatorHIPRAND_STATUS_SUCCESS if random numbers were successfully generated
-
hiprandStatus_t hiprandGenerateSeeds(hiprandGenerator_t generator)#
Initializes the generator’s state on GPU or host.
Initializes the generator’s state on GPU or host.
If hiprandGenerateSeeds() was not called for a generator, it will be automatically called by functions which generates random numbers like hiprandGenerate(), hiprandGenerateUniform(), hiprandGenerateNormal() etc.
- Parameters:
generator – - Generator to initialize
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was never created
HIPRAND_STATUS_PREEXISTING_FAILURE if there was an existing error from a previous kernel launch
HIPRAND_STATUS_LAUNCH_FAILURE if the kernel launch failed for any reason
HIPRAND_STATUS_SUCCESS if the seeds were generated successfully
-
hiprandStatus_t hiprandSetStream(hiprandGenerator_t generator, hipStream_t stream)#
Sets the current stream for kernel launches.
Sets the current stream for all kernel launches of the generator. All functions will use this stream.
- Parameters:
generator – - Generator to modify
stream – - Stream to use or NULL for default stream
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_SUCCESS if stream was set successfully
-
hiprandStatus_t hiprandSetPseudoRandomGeneratorSeed(hiprandGenerator_t generator, unsigned long long seed)#
Sets the seed of a pseudo-random number generator.
Sets the seed of the pseudo-random number generator.
This operation resets the generator’s internal state.
This operation does not change the generator’s offset.
- Parameters:
generator – - Pseudo-random number generator
seed – - New seed value
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_TYPE_ERROR if the generator is a quasi random number generator
HIPRAND_STATUS_SUCCESS if seed was set successfully
-
hiprandStatus_t hiprandSetGeneratorOffset(hiprandGenerator_t generator, unsigned long long offset)#
Sets the offset of a random number generator.
Sets the absolute offset of the random number generator.
This operation resets the generator’s internal state.
This operation does not change the generator’s seed.
Absolute offset cannot be set if generator’s type is HIPRAND_RNG_PSEUDO_MTGP32 or HIPRAND_RNG_PSEUDO_MT19937.
- Parameters:
generator – - Random number generator
offset – - New absolute offset
- Returns:
HIPRAND_STATUS_NOT_INITIALIZED if the generator was not initialized
HIPRAND_STATUS_SUCCESS if offset was successfully set
HIPRAND_STATUS_TYPE_ERROR if generator’s type is HIPRAND_RNG_PSEUDO_MTGP32 or HIPRAND_RNG_PSEUDO_MT19937
-
hiprandStatus_t hiprandSetQuasiRandomGeneratorDimensions(hiprandGenerator_t generator, unsigned int dimensions)#
Set the number of dimensions of a quasi-random number generator.
Set the number of dimensions of a quasi-random number generator. Supported values of
dimensions
are 1 to 20000.This operation resets the generator’s internal state.
This operation does not change the generator’s offset.
- Parameters:
generator – - Quasi-random number generator
dimensions – - Number of dimensions
- Returns:
HIPRAND_STATUS_NOT_CREATED if the generator wasn’t created
HIPRAND_STATUS_TYPE_ERROR if the generator is not a quasi-random number generator
HIPRAND_STATUS_OUT_OF_RANGE if
dimensions
is out of rangeHIPRAND_STATUS_SUCCESS if the number of dimensions was set successfully
-
hiprandStatus_t hiprandGetVersion(int *version)#
Returns the version number of the cuRAND or rocRAND library.
Returns in
version
the version number of the underlying cuRAND or rocRAND library.- Parameters:
version – - Version of the library
- Returns:
HIPRAND_STATUS_OUT_OF_RANGE if
version
is NULLHIPRAND_STATUS_SUCCESS if the version number was successfully returned
-
hiprandStatus_t hiprandCreatePoissonDistribution(double lambda, hiprandDiscreteDistribution_t *discrete_distribution)#
Construct the histogram for a Poisson distribution.
Construct the histogram for the Poisson distribution with lambda
lambda
.- Parameters:
lambda – - lambda for the Poisson distribution
discrete_distribution – - pointer to the histogram in device memory
- Returns:
HIPRAND_STATUS_ALLOCATION_FAILED if memory could not be allocated
HIPRAND_STATUS_OUT_OF_RANGE if
discrete_distribution
pointer was nullHIPRAND_STATUS_OUT_OF_RANGE if lambda is non-positive
HIPRAND_STATUS_SUCCESS if the histogram was constructed successfully
-
hiprandStatus_t hiprandDestroyDistribution(hiprandDiscreteDistribution_t discrete_distribution)#
Destroy the histogram array for a discrete distribution.
Destroy the histogram array for a discrete distribution created by hiprandCreatePoissonDistribution.
- Parameters:
discrete_distribution – - pointer to the histogram in device memory
- Returns:
HIPRAND_STATUS_OUT_OF_RANGE if
discrete_distribution
was nullHIPRAND_STATUS_SUCCESS if the histogram was destroyed successfully
-
HIPRAND_VERSION#
C++ Host API Wrapper#
- group hiprandhostcpp
Typedefs
-
typedef philox4x32_10_engine philox4x32_10#
Typedef of hiprand_cpp::philox4x32_10_engine PRNG engine with default seed (HIPRAND_PHILOX4x32_DEFAULT_SEED).
-
typedef xorwow_engine xorwow#
Typedef of hiprand_cpp::xorwow_engine PRNG engine with default seed (HIPRAND_XORWOW_DEFAULT_SEED).
-
typedef mrg32k3a_engine mrg32k3a#
Typedef of hiprand_cpp::mrg32k3a_engine PRNG engine with default seed (HIPRAND_MRG32K3A_DEFAULT_SEED).
-
typedef mtgp32_engine mtgp32#
Typedef of hiprand_cpp::mtgp32_engine PRNG engine with default seed (HIPRAND_MTGP32_DEFAULT_SEED).
-
typedef mt19937_engine mt19937#
Typedef of hiprand_cpp::mt19937_engine PRNG engine with default seed (HIPRAND_MT19937_DEFAULT_SEED).
-
typedef sobol32_engine sobol32#
Typedef of hiprand_cpp::sobol32_engine QRNG engine with default number of dimensions (1).
-
typedef scrambled_sobol32_engine scrambled_sobol32#
Typedef of hiprand_cpp::scrambled_sobol32_engine QRNG engine with default number of dimensions (1).
-
typedef sobol64_engine sobol64#
Typedef of hiprand_cpp::sobol64_engine QRNG engine with default number of dimensions (1).
-
typedef scrambled_sobol64_engine scrambled_sobol64#
Typedef of hiprand_cpp::scrambled_sobol64_engine QRNG engine with default number of dimensions (1).
-
typedef std::random_device random_device#
A non-deterministic uniform random number generator.
hiprand_cpp::random_device is non-deterministic uniform random number generator, or a pseudo-random number engine if there is no support for non-deterministic random number generation. It’s implemented as a typedef of std::random_device.
For practical use hiprand_cpp::random_device is generally only used to seed a PRNG such as hiprand_cpp::mtgp32_engine.
Example:
#include <hiprand/hiprand.hpp> int main() { const size_t size = 8192; unsigned int * output; hipMalloc(&output, size * sizeof(unsigned int)); hiprand_cpp::random_device rd; hiprand_cpp::mtgp32 engine(rd()); // seed engine with a real random value, if available hiprand_cpp::normal_distribution<float> dist(0.0, 1.5); dist(engine, output, size); }
Functions
-
inline int version()#
Returns hipRAND version.
- Returns:
hipRAND version number as an
int
value.
-
class error : public std::exception#
- #include <hiprand.hpp>
A run-time hipRAND error.
The error class represents an error returned by a hipRAND function.
-
template<class IntType = unsigned int>
class uniform_int_distribution# - #include <hiprand.hpp>
Produces random integer values uniformly distributed on the interval [0, 2^(sizeof(IntType)*8) - 1].
- Template Parameters:
IntType – - type of generated values. Only
unsigned char
,unsigned short
,unsigned int
,unsigned long long int
are supported.
-
template<class RealType = float>
class uniform_real_distribution# - #include <hiprand.hpp>
Produces random floating-point values uniformly distributed on the interval (0, 1].
- Template Parameters:
RealType – - type of generated values. Only
float
,double
andhalf
types are supported.
-
template<class RealType = float>
class normal_distribution# - #include <hiprand.hpp>
Produces random numbers according to a normal distribution.
- Template Parameters:
RealType – - type of generated values. Only
float
,double
andhalf
types are supported.
-
template<class RealType = float>
class lognormal_distribution# - #include <hiprand.hpp>
Produces positive random numbers according to a log-normal distribution.
- Template Parameters:
RealType – - type of generated values. Only
float
,double
andhalf
types are supported.
-
template<class IntType = unsigned int>
class poisson_distribution# - #include <hiprand.hpp>
Produces random non-negative integer values distributed according to Poisson distribution.
- Template Parameters:
IntType – - type of generated values. Only
unsinged
int
type is supported.
-
template<unsigned long long DefaultSeed = HIPRAND_PHILOX4x32_DEFAULT_SEED>
class philox4x32_10_engine# - #include <hiprand.hpp>
Pseudorandom number engine based Philox algorithm.
philox4x32_10_engine implements a Counter-based random number generator called Philox, which was developed by a group at D. E. Shaw Research. It generates random numbers of type
unsigned
int
on the interval [0; 2^32 - 1]. Random numbers are generated in sets of four.
-
template<unsigned long long DefaultSeed = HIPRAND_XORWOW_DEFAULT_SEED>
class xorwow_engine# - #include <hiprand.hpp>
Pseudorandom number engine based XORWOW algorithm.
xorwow_engine is a xorshift pseudorandom number engine based on XORWOW algorithm, which was presented by George Marsaglia in “Xorshift RNGs” paper published in Journal of Statistical Software. It produces random numbers of type
unsigned
int
on the interval [0; 2^32 - 1].
-
template<unsigned long long DefaultSeed = HIPRAND_MRG32K3A_DEFAULT_SEED>
class mrg32k3a_engine# - #include <hiprand.hpp>
Pseudorandom number engine based MRG32k3a CMRG.
mrg32k3a_engine is an implementation of MRG32k3a pseudorandom number generator, which is a Combined Multiple Recursive Generator (CMRG) created by Pierre L’Ecuyer. It produces random 32-bit
unsigned
int
values on the interval [0; 2^32 - 1].
-
template<unsigned long long DefaultSeed = HIPRAND_MTGP32_DEFAULT_SEED>
class mtgp32_engine# - #include <hiprand.hpp>
Pseudorandom number engine based on Mersenne Twister for Graphic Processors algorithm.
mtgp32_engine is a random number engine based on Mersenne Twister for Graphic Processors algorithm, which is a version of well-known Mersenne Twister algorithm. It produces high quality random numbers of type
unsigned
int
on the interval [0; 2^32 - 1].
-
template<unsigned long long DefaultSeed = HIPRAND_MT19937_DEFAULT_SEED>
class mt19937_engine# - #include <hiprand.hpp>
Pseudorandom number engine based on Mersenne Twister.
mt19937_engine is a random number engine based on the well-known Mersenne Twister algorithm. It produces high quality random numbers of type
unsigned
int
on the interval [0; 2^32 - 1].
-
template<unsigned int DefaultNumDimensions = 1>
class sobol32_engine# - #include <hiprand.hpp>
Sobol’s quasi-random sequence generator.
sobol32_engine is quasi-random number engine which produced Sobol sequences. This implementation supports generating sequences in up to 20,000 dimensions. The engine produces random unsigned integers on the interval [0; 2^32 - 1].
-
template<unsigned int DefaultNumDimensions = 1>
class scrambled_sobol32_engine# - #include <hiprand.hpp>
Sobol’s quasi-random sequence generator.
scrambled_sobol32_engine is a quasi-random number engine which produces scrambled Sobol sequences. This implementation supports generating sequences in up to 20,000 dimensions. The engine produces random unsigned integers on the interval [0; 2^32 - 1].
-
template<unsigned int DefaultNumDimensions = 1>
class sobol64_engine# - #include <hiprand.hpp>
Sobol’s quasi-random sequence generator.
sobol64_engine is a quasi-random number engine which produced Sobol sequences. This implementation supports generating sequences in up to 20,000 dimensions. The engine produces random unsigned integers on the interval [0; 2^64 - 1].
-
template<unsigned int DefaultNumDimensions = 1>
class scrambled_sobol64_engine# - #include <hiprand.hpp>
Sobol’s quasi-random sequence generator.
scrambled_sobol64_engine is a quasi-random number engine which produces scrambled Sobol sequences. This implementation supports generating sequences in up to 20,000 dimensions. The engine produces random unsigned integers on the interval [0; 2^64 - 1].
-
typedef philox4x32_10_engine philox4x32_10#