pycuda.gpuarray.
vec
¶All of CUDA’s supported vector types, such as float3 and long4 are
available as numpy
data types within this class. These
numpy.dtype
instances have field names of x, y, z, and w
just like their CUDA counterparts. They will work both for parameter passing
to kernels as well as for passing data back and forth between kernels and
Python code. For each type, a make_type function is also provided (e.g.
make_float3(x,y,z)).
GPUArray
Array Class¶pycuda.gpuarray.
GPUArray
(shape, dtype, *, allocator=None, order="C")¶A numpy.ndarray
workalike that stores its data and performs its
computations on the compute device. shape and dtype work exactly as in
numpy
. Arithmetic methods in GPUArray
support the
broadcasting of scalars. (e.g. array+5) If the
allocator is a callable that, upon being called with an argument of the number
of bytes to be allocated, returns an object that can be cast to an
int
representing the address of the newly allocated memory.
Observe that both pycuda.driver.mem_alloc()
and
pycuda.tools.DeviceMemoryPool.alloc()
are a model of this interface.
All arguments beyond allocator should be considered keywordonly.
gpudata
¶The pycuda.driver.DeviceAllocation
instance created for the memory that backs
this GPUArray
.
shape
¶The tuple of lengths of each dimension in the array.
dtype
¶The numpy.dtype
of the items in the GPU array.
size
¶The number of meaningful entries in the array. Can also be computed by
multiplying up the numbers in shape
.
mem_size
¶The total number of entries, including padding, that are present in
the array. Padding may arise for example because of pitch adjustment by
pycuda.driver.mem_alloc_pitch()
.
strides
¶Tuple of bytes to step in each dimension when traversing an array.
flags
¶Return an object with attributes c_contiguous, f_contiguous and forc,
which may be used to query contiguity properties in analogy to
numpy.ndarray.flags
.
__len__
()¶Returns the size of the leading dimension of self.
reshape
(shape)¶Returns an array containing the same data with a new shape.
ravel
()¶Returns flattened array containing the same data.
view
(dtype=None)¶Returns view of array with the same data. If dtype is different from current dtype, the actual bytes of memory will be reinterpreted.
set
(ary)¶Transfer the contents the numpy.ndarray
object ary
onto the device.
ary must have the same dtype and size (not necessarily shape) as self.
set_async
(ary, stream=None)¶Asynchronously transfer the contents the numpy.ndarray
object ary
onto the device, optionally sequenced on stream.
ary must have the same dtype and size (not necessarily shape) as self.
get
(ary=None, pagelocked=False)¶Transfer the contents of self into ary or a newly allocated
numpy.ndarray
. If ary is given, it must have the same
shape and dtype. If it is not given,
a pagelocked specifies whether the new array is allocated
pagelocked.
Changed in version 2015.2: ary with different shape was deprecated.
get_async
(stream=None, ary=None)¶Transfer the contents of self into ary or a newly allocated
numpy.ndarray
. If ary is given, it must have the right
size (not necessarily shape) and dtype. If it is not given,
a pagelocked array is newly allocated.
copy
()¶New in version 2013.1.
mul_add(self, selffac, other, otherfac, add_timer=None, stream=None):
Return selffac*self + otherfac*other. add_timer, if given,
is invoked with the result from
pycuda.driver.Function.prepared_timed_call()
.
__add__
(other)¶__sub__
(other)¶__iadd__
(other)¶__isub__
(other)¶__neg__
(other)¶__mul__
(other)¶__div__
(other)¶__rdiv__
(other)¶__pow__
(other)¶fill
(scalar, stream=None)¶Fill the array with scalar.
astype
(dtype, stream=None)¶Return self, cast to dtype.
real
¶Return the real part of self, or self if it is real.
New in version 0.94.
imag
¶Return the imaginary part of self, or zeros_like(self) if it is real.
conj
()¶Return the complex conjugate of self, or self if it is real.
bind_to_texref
(texref, allow_offset=False)¶Bind self to the pycuda.driver.TextureReference
texref.
Due to alignment requirements, the effective texture bind address may be
different from the requested one by an offset. This method returns this
offset in units of self‘s data type. If allow_offset is False
, a
nonzero value of this offset will cause an exception to be raised.
Note
It is recommended to use bind_to_texref_ext()
instead of
this method.
bind_to_texref_ext
(texref, channels=1, allow_double_hack=False, allow_offset=False)¶Bind self to the pycuda.driver.TextureReference
texref.
In addition, set the texture reference’s format to match dtype
and its channel count to channels. This routine also sets the
texture reference’s pycuda.driver.TRSF_READ_AS_INTEGER
flag,
if necessary.
Due to alignment requirements, the effective texture bind address may be
different from the requested one by an offset. This method returns this
offset in units of self‘s data type. If allow_offset is False
, a
nonzero value of this offset will cause an exception to be raised.
New in version 0.93.
As of this writing, CUDA textures do not natively support doubleprecision floating point data. To remedy this deficiency, PyCUDA contains a workaround, which can be enabled by passing True for allow_double_hack. In this case, use the following code for texture access in your kernel code:
#include <pycudahelpers.hpp>
texture<fp_tex_double, 1, cudaReadModeElementType> my_tex;
__global__ void f()
{
...
fp_tex1Dfetch(my_tex, threadIdx.x);
...
}
(This workaround was added in version 0.94.)
GPUArray
Instances¶pycuda.gpuarray.
to_gpu
(ary, allocator=None)¶Return a GPUArray
that is an exact copy of the numpy.ndarray
instance ary.
See GPUArray
for the meaning of allocator.
pycuda.gpuarray.
to_gpu_async
(ary, allocator=None, stream=None)¶Return a GPUArray
that is an exact copy of the numpy.ndarray
instance ary. The copy is done asynchronously, optionally sequenced into
stream.
See GPUArray
for the meaning of allocator.
pycuda.gpuarray.
empty
(shape, dtype, *, allocator=None, order="C")¶A synonym for the GPUArray
constructor.
pycuda.gpuarray.
zeros
(shape, dtype, *, allocator=None, order="C")¶Same as empty()
, but the GPUArray
is zeroinitialized before
being returned.
pycuda.gpuarray.
empty_like
(other_ary)¶Make a new, uninitialized GPUArray
having the same properties
as other_ary.
pycuda.gpuarray.
zeros_like
(other_ary)¶Make a new, zeroinitialized GPUArray
having the same properties
as other_ary.
pycuda.gpuarray.
arange
(start, stop, step, dtype=None, stream=None)¶Create a GPUArray
filled with numbers spaced step apart,
starting from start and ending at stop.
For floating point arguments, the length of the result is ceil((stop  start)/step). This rule may result in the last element of the result being greater than stop.
dtype, if not specified, is taken as the largest common type of start, stop and step.
pycuda.gpuarray.
take
(a, indices, stream=None)¶Return the GPUArray
[a[indices[0]], ..., a[indices[n]]]
.
For the moment, a must be a type that can be bound to a texture.
pycuda.gpuarray.
if_positive
(criterion, then_, else_, out=None, stream=None)¶Return an array like then_, which, for the element at index i, contains then_[i] if criterion[i]>0, else else_[i]. (added in 0.94)
pycuda.gpuarray.
maximum
(a, b, out=None, stream=None)¶Return the elementwise maximum of a and b. (added in 0.94)
pycuda.gpuarray.
minimum
(a, b, out=None, stream=None)¶Return the elementwise minimum of a and b. (added in 0.94)
pycuda.gpuarray.
sum
(a, dtype=None, stream=None)¶pycuda.gpuarray.
subset_sum
(subset, a, dtype=None, stream=None)¶New in version 2013.1.
pycuda.gpuarray.
dot
(a, b, dtype=None, stream=None)¶pycuda.gpuarray.
subset_dot
(subset, a, b, dtype=None, stream=None)¶pycuda.gpuarray.
max
(a, stream=None)¶pycuda.gpuarray.
min
(a, stream=None)¶pycuda.gpuarray.
subset_max
(subset, a, stream=None)¶pycuda.gpuarray.
subset_min
(subset, a, stream=None)¶GPUArrray
Instances¶The pycuda.cumath
module contains elementwise
workalikes for the functions contained in math
.
pycuda.cumath.
fabs
(array, *, out=None, stream=None)¶pycuda.cumath.
ceil
(array, *, out=None, stream=None)¶pycuda.cumath.
floor
(array, *, out=None, stream=None)¶pycuda.cumath.
exp
(array, *, out=None, stream=None)¶pycuda.cumath.
log
(array, *, out=None, stream=None)¶pycuda.cumath.
log10
(array, *, out=None, stream=None)¶pycuda.cumath.
sqrt
(array, *, out=None, stream=None)¶pycuda.cumath.
sin
(array, *, out=None, stream=None)¶pycuda.cumath.
cos
(array, *, out=None, stream=None)¶pycuda.cumath.
tan
(array, *, out=None, stream=None)¶pycuda.cumath.
asin
(array, *, out=None, stream=None)¶pycuda.cumath.
acos
(array, *, out=None, stream=None)¶pycuda.cumath.
atan
(array, *, out=None, stream=None)¶pycuda.cumath.
sinh
(array, *, out=None, stream=None)¶pycuda.cumath.
cosh
(array, *, out=None, stream=None)¶pycuda.cumath.
tanh
(array, *, out=None, stream=None)¶pycuda.cumath.
fmod
(arg, mod, stream=None)¶Return the floating point remainder of the division arg/mod, for each element in arg and mod.
pycuda.cumath.
frexp
(arg, stream=None)¶Return a tuple (significands, exponents) such that arg == significand * 2**exponent.
pycuda.cumath.
ldexp
(significand, exponent, stream=None)¶Return a new array of floating point values composed from the entries of significand and exponent, paired together as result = significand * 2**exponent.
pycuda.cumath.
modf
(arg, stream=None)¶Return a tuple (fracpart, intpart) of arrays containing the integer and fractional parts of arg.
pycuda.curandom.
rand
(shape, dtype=numpy.float32, stream=None)¶Return an array of shape filled with random values of dtype in the range [0,1).
Note
The use case for this function is “I need some random numbers. I don’t care how good they are or how fast I get them.” It uses a pretty terrible MD5based generator and doesn’t even attempt to cache generated code.
If you’re interested in a nontoy random number generator, use the CURANDbased functionality below.
Warning
The following classes are using random number generators that run on the GPU. Each thread uses its own generator. Creation of those generators requires more resources than subsequent generation of random numbers. After experiments it looks like maximum number of active generators on Tesla devices (with compute capabilities 1.x) is 256. Fermi devices allow for creating 1024 generators without any problems. If there are troubles with creating objects of class PseudoRandomNumberGenerator or QuasiRandomNumberGenerator decrease number of created generators (and therefore number of active threads).
A pseudorandom sequence of numbers satisfies most of the statistical properties of a truly random sequence but is generated by a deterministic algorithm. A quasirandom sequence of ndimensional points is generated by a deterministic algorithm designed to fill an ndimensional space evenly.
Quasirandom numbers are more expensive to generate.
pycuda.curandom.
get_curand_version
()¶Obtain the version of CURAND against which PyCUDA was compiled. Returns a 3tuple of integers as (major, minor, revision).
pycuda.curandom.
seed_getter_uniform
(N)¶Return an GPUArray
filled with one random int32 repeated N
times which can be used as a seed for XORWOW generator.
pycuda.curandom.
seed_getter_unique
(N)¶Return an GPUArray
filled with N random int32 which can
be used as a seed for XORWOW generator.
pycuda.curandom.
XORWOWRandomNumberGenerator
(seed_getter=None, offset=0)¶Parameters: 


Provides pseudorandom numbers. Generates sequences with period at least \(2^190\).
CUDA 3.2 and above.
New in version 2011.1.
fill_uniform
(data, stream=None)¶Fills in GPUArray
data with uniformly distributed
pseudorandom values.
gen_uniform
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with uniformly distributed pseudorandom values,
and returns newly created object.
fill_normal
(data, stream=None)¶Fills in GPUArray
data with normally distributed
pseudorandom values.
gen_normal
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with normally distributed pseudorandom values,
and returns newly created object.
fill_log_normal
(data, mean, stddev, stream=None)¶Fills in GPUArray
data with lognormally distributed
pseudorandom values with mean mean and standard deviation stddev.
CUDA 4.0 and above.
New in version 2012.2.
gen_log_normal
(shape, dtype, mean, stddev, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with lognormally distributed pseudorandom values
with mean mean and standard deviation stddev, and returns
newly created object.
CUDA 4.0 and above.
New in version 2012.2.
fill_poisson
(data, lambda_value, stream=None)¶Fills in GPUArray
data with Poisson distributed
pseudorandom values with lambda lambda_value. data must
be of type 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
gen_poisson
(shape, dtype, lambda_value, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with Poisson distributed pseudorandom values
with lambda lambda_value, and returns newly created object.
dtype must be 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
call_skip_ahead
(i, stream=None)¶Forces all generators to skip i values. Is equivalent to generating i values and discarding results, but is much faster.
call_skip_ahead_array
(i, stream=None)¶Accepts array i of integer values, telling each generator how many values to skip.
call_skip_ahead_sequence
(i, stream=None)¶Forces all generators to skip i subsequences. Is equivalent to generating i * \(2^67\) values and discarding results, but is much faster.
call_skip_ahead_sequence_array
(i, stream=None)¶Accepts array i of integer values, telling each generator how many subsequences to skip.
pycuda.curandom.
MRG32k3aRandomNumberGenerator
(seed_getter=None, offset=0)¶Parameters: 


Provides pseudorandom numbers. Generates sequences with period at least \(2^190\).
CUDA 4.1 and above.
New in version 2013.1.
fill_uniform
(data, stream=None)¶Fills in GPUArray
data with uniformly distributed
pseudorandom values.
gen_uniform
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with uniformly distributed pseudorandom values,
and returns newly created object.
fill_normal
(data, stream=None)¶Fills in GPUArray
data with normally distributed
pseudorandom values.
gen_normal
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with normally distributed pseudorandom values,
and returns newly created object.
fill_log_normal
(data, mean, stddev, stream=None)¶Fills in GPUArray
data with lognormally distributed
pseudorandom values with mean mean and standard deviation stddev.
gen_log_normal
(shape, dtype, mean, stddev, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with lognormally distributed pseudorandom values
with mean mean and standard deviation stddev, and returns
newly created object.
fill_poisson
(data, lambda_value, stream=None)¶Fills in GPUArray
data with Poisson distributed
pseudorandom values with lambda lambda_value. data must
be of type 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
gen_poisson
(shape, dtype, lambda_value, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with Poisson distributed pseudorandom values
with lambda lambda_value, and returns newly created object.
dtype must be 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
call_skip_ahead
(i, stream=None)¶Forces all generators to skip i values. Is equivalent to generating i values and discarding results, but is much faster.
call_skip_ahead_array
(i, stream=None)¶Accepts array i of integer values, telling each generator how many values to skip.
call_skip_ahead_sequence
(i, stream=None)¶Forces all generators to skip i subsequences. Is equivalent to generating i * \(2^67\) values and discarding results, but is much faster.
call_skip_ahead_sequence_array
(i, stream=None)¶Accepts array i of integer values, telling each generator how many subsequences to skip.
pycuda.curandom.
generate_direction_vectors
(count, direction=direction_vector_set.VECTOR_32)¶Return an GPUArray
count filled with direction vectors
used to initialize Sobol generators.
pycuda.curandom.
generate_scramble_constants32
(count)¶Return a GPUArray
filled with count’ 32bit unsigned integer
numbers used to initialize :class:`ScrambledSobol32RandomNumberGenerator
pycuda.curandom.
generate_scramble_constants64
(count)¶Return a GPUArray
filled with count’ 64bit unsigned integer
numbers used to initialize :class:`ScrambledSobol64RandomNumberGenerator
pycuda.curandom.
Sobol32RandomNumberGenerator
(dir_vector=None, offset=0)¶Parameters: 


Provides quasirandom numbers. Generates sequences with period of \(2^32\).
CUDA 3.2 and above.
New in version 2011.1.
fill_uniform
(data, stream=None)¶Fills in GPUArray
data with uniformly distributed
quasirandom values.
gen_uniform
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with uniformly distributed pseudorandom values,
and returns newly created object.
fill_normal
(data, stream=None)¶Fills in GPUArray
data with normally distributed
quasirandom values.
gen_normal
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with normally distributed pseudorandom values,
and returns newly created object.
fill_log_normal
(data, mean, stddev, stream=None)¶Fills in GPUArray
data with lognormally distributed
pseudorandom values with mean mean and standard deviation stddev.
CUDA 4.0 and above.
New in version 2012.2.
gen_log_normal
(shape, dtype, mean, stddev, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with lognormally distributed pseudorandom values
with mean mean and standard deviation stddev, and returns
newly created object.
CUDA 4.0 and above.
New in version 2012.2.
fill_poisson
(data, lambda_value, stream=None)¶Fills in GPUArray
data with Poisson distributed
pseudorandom values with lambda lambda_value. data must
be of type 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
gen_poisson
(shape, dtype, lambda_value, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with Poisson distributed pseudorandom values
with lambda lambda_value, and returns newly created object.
dtype must be 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
call_skip_ahead
(i, stream=None)¶Forces all generators to skip i values. Is equivalent to generating i values and discarding results, but is much faster.
call_skip_ahead_array
(i, stream=None)¶Accepts array i of integer values, telling each generator how many values to skip.
pycuda.curandom.
ScrambledSobol32RandomNumberGenerator
(dir_vector=None, scramble_vector=None, offset=0)¶Parameters: 


Provides quasirandom numbers. Generates sequences with period of \(2^32\).
CUDA 4.0 and above.
New in version 2011.1.
fill_uniform
(data, stream=None)¶Fills in GPUArray
data with uniformly distributed
quasirandom values.
gen_uniform
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with uniformly distributed pseudorandom values,
and returns newly created object.
fill_normal
(data, stream=None)¶Fills in GPUArray
data with normally distributed
quasirandom values.
gen_normal
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with normally distributed pseudorandom values,
and returns newly created object.
fill_log_normal
(data, mean, stddev, stream=None)¶Fills in GPUArray
data with lognormally distributed
pseudorandom values with mean mean and standard deviation stddev.
CUDA 4.0 and above.
New in version 2012.2.
gen_log_normal
(shape, dtype, mean, stddev, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with lognormally distributed pseudorandom values
with mean mean and standard deviation stddev, and returns
newly created object.
CUDA 4.0 and above.
New in version 2012.2.
fill_poisson
(data, lambda_value, stream=None)¶Fills in GPUArray
data with Poisson distributed
pseudorandom values with lambda lambda_value. data must
be of type 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
gen_poisson
(shape, dtype, lambda_value, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with Poisson distributed pseudorandom values
with lambda lambda_value, and returns newly created object.
dtype must be 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
call_skip_ahead
(i, stream=None)¶Forces all generators to skip i values. Is equivalent to generating i values and discarding results, but is much faster.
call_skip_ahead_array
(i, stream=None)¶Accepts array i of integer values, telling each generator how many values to skip.
pycuda.curandom.
Sobol64RandomNumberGenerator
(dir_vector=None, offset=0)¶Parameters: 


Provides quasirandom numbers. Generates sequences with period of \(2^64\).
CUDA 4.0 and above.
New in version 2011.1.
fill_uniform
(data, stream=None)¶Fills in GPUArray
data with uniformly distributed
quasirandom values.
gen_uniform
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with uniformly distributed pseudorandom values,
and returns newly created object.
fill_normal
(data, stream=None)¶Fills in GPUArray
data with normally distributed
quasirandom values.
gen_normal
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with normally distributed pseudorandom values,
and returns newly created object.
fill_log_normal
(data, mean, stddev, stream=None)¶Fills in GPUArray
data with lognormally distributed
pseudorandom values with mean mean and standard deviation stddev.
CUDA 4.0 and above.
New in version 2012.2.
gen_log_normal
(shape, dtype, mean, stddev, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with lognormally distributed pseudorandom values
with mean mean and standard deviation stddev, and returns
newly created object.
CUDA 4.0 and above.
New in version 2012.2.
fill_poisson
(data, lambda_value, stream=None)¶Fills in GPUArray
data with Poisson distributed
pseudorandom values with lambda lambda_value. data must
be of type 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
gen_poisson
(shape, dtype, lambda_value, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with Poisson distributed pseudorandom values
with lambda lambda_value, and returns newly created object.
dtype must be 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
call_skip_ahead
(i, stream=None)¶Forces all generators to skip i values. Is equivalent to generating i values and discarding results, but is much faster.
call_skip_ahead_array
(i, stream=None)¶Accepts array i of integer values, telling each generator how many values to skip.
pycuda.curandom.
ScrambledSobol64RandomNumberGenerator
(dir_vector=None, scramble_vector=None, offset=0)¶Parameters: 


Provides quasirandom numbers. Generates sequences with period of \(2^64\).
CUDA 4.0 and above.
New in version 2011.1.
fill_uniform
(data, stream=None)¶Fills in GPUArray
data with uniformly distributed
quasirandom values.
gen_uniform
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with uniformly distributed pseudorandom values,
and returns newly created object.
fill_normal
(data, stream=None)¶Fills in GPUArray
data with normally distributed
quasirandom values.
gen_normal
(shape, dtype, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with normally distributed pseudorandom values,
and returns newly created object.
fill_log_normal
(data, mean, stddev, stream=None)¶Fills in GPUArray
data with lognormally distributed
pseudorandom values with mean mean and standard deviation stddev.
CUDA 4.0 and above.
New in version 2012.2.
gen_log_normal
(shape, dtype, mean, stddev, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with lognormally distributed pseudorandom values
with mean mean and standard deviation stddev, and returns
newly created object.
CUDA 4.0 and above.
New in version 2012.2.
fill_poisson
(data, lambda_value, stream=None)¶Fills in GPUArray
data with Poisson distributed
pseudorandom values with lambda lambda_value. data must
be of type 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
gen_poisson
(shape, dtype, lambda_value, stream=None)¶Creates object of GPUArray
with given shape and dtype,
fills it in with Poisson distributed pseudorandom values
with lambda lambda_value, and returns newly created object.
dtype must be 32bit unsigned int.
CUDA 5.0 and above.
New in version 2013.1.
call_skip_ahead
(i, stream=None)¶Forces all generators to skip i values. Is equivalent to generating i values and discarding results, but is much faster.
call_skip_ahead_array
(i, stream=None)¶Accepts array i of integer values, telling each generator how many values to skip.
Evaluating involved expressions on GPUArray
instances can be
somewhat inefficient, because a new temporary is created for each
intermediate result. The functionality in the module pycuda.elementwise
contains tools to help generate kernels that evaluate multistage expressions
on one or several operands in a single pass.
pycuda.elementwise.
ElementwiseKernel
(arguments, operation, name="kernel", keep=False, options=[], preamble="")¶Generate a kernel that takes a number of scalar or vector arguments and performs the scalar operation on each entry of its arguments, if that argument is a vector.
arguments is specified as a string formatted as a C argument list. operation is specified as a C assignment statement, without a semicolon. Vectors in operation should be indexed by the variable i.
name specifies the name as which the kernel is compiled, keep
and options are passed unmodified to pycuda.compiler.SourceModule
.
preamble specifies some source code that is included before the elementwise kernel specification. You may use this to include other files and/or define functions that are used by operation.
__call__
(*args, range=None, slice=None)¶Invoke the generated scalar kernel. The arguments may either be scalars or
GPUArray
instances.
If range is given, it must be a slice
object and specifies
the range of indices i for which the operation is carried out.
If slice is given, it must be a slice
object and specifies
the range of indices i for which the operation is carried out,
truncated to the container. Also, slice may contain negative indices
to index relative to the end of the array.
If stream is given, it must be a pycuda.driver.Stream
object,
where the execution will be serialized.
Here’s a usage example:
import pycuda.gpuarray as gpuarray
import pycuda.driver as cuda
import pycuda.autoinit
import numpy
from pycuda.curandom import rand as curand
a_gpu = curand((50,))
b_gpu = curand((50,))
from pycuda.elementwise import ElementwiseKernel
lin_comb = ElementwiseKernel(
"float a, float *x, float b, float *y, float *z",
"z[i] = a*x[i] + b*y[i]",
"linear_combination")
c_gpu = gpuarray.empty_like(a_gpu)
lin_comb(5, a_gpu, 6, b_gpu, c_gpu)
import numpy.linalg as la
assert la.norm((c_gpu  (5*a_gpu+6*b_gpu)).get()) < 1e5
(You can find this example as examples/demo_elementwise.py
in the PyCuda
distribution.)
pycuda.reduction.
ReductionKernel
(dtype_out, neutral, reduce_expr, map_expr=None, arguments=None, name="reduce_kernel", keep=False, options=[], preamble="", allocator=None)¶Generate a kernel that takes a number of scalar or vector arguments (at least one vector argument), performs the map_expr on each entry of the vector argument and then the reduce_expr on the outcome of that. neutral serves as an initial value. preamble offers the possibility to add preprocessor directives and other code (such as helper functions) to be added before the actual reduction kernel code.
Vectors in map_expr should be indexed by the variable i. reduce_expr uses the formal values “a” and “b” to indicate two operands of a binary reduction operation. If you do not specify a map_expr, “in[i]” – and therefore the presence of only one input argument – is automatically assumed.
dtype_out specifies the numpy.dtype
in which the reduction is
performed and in which the result is returned. neutral is
specified as float or integer formatted as string. reduce_expr and
map_expr are specified as string formatted operations and arguments
is specified as a string formatted as a C argument list. name specifies
the name as which the kernel is compiled, keep and options are passed
unmodified to pycuda.compiler.SourceModule
. preamble is specified
as a string of code.
Here’s a usage example:
a = gpuarray.arange(400, dtype=numpy.float32)
b = gpuarray.arange(400, dtype=numpy.float32)
krnl = ReductionKernel(numpy.float32, neutral="0",
reduce_expr="a+b", map_expr="x[i]*y[i]",
arguments="float *x, float *y")
my_dot_prod = krnl(a, b).get()
pycuda.scan.
ExclusiveScanKernel
(dtype, scan_expr, neutral, name_prefix="scan", options=[], preamble="")¶Generates a kernel that can compute a prefix sum using any associative operation given as scan_expr. scan_expr uses the formal values “a” and “b” to indicate two operands of an associative binary operation. neutral is the neutral element of scan_expr, obeying scan_expr(a, neutral) == a.
dtype specifies the type of the arrays being operated on. name_prefix is used for kernel names to ensure recognizability in profiles and logs. options is a list of compiler options to use when building. preamble specifies a string of code that is inserted before the actual kernels.
__call__
(self, input_ary, output_ary=None, allocator=None, queue=None)¶pycuda.scan.
InclusiveScanKernel
(dtype, scan_expr, neutral=None, name_prefix="scan", options=[], preamble="", devices=None)¶Works like ExclusiveScanKernel
. Unlike the exclusive case,
neutral is not required.
Here’s a usage example:
knl = InclusiveScanKernel(np.int32, "a+b")
n = 2**202**18+5
host_data = np.random.randint(0, 10, n).astype(np.int32)
dev_data = gpuarray.to_gpu(queue, host_data)
knl(dev_data)
assert (dev_data.get() == np.cumsum(host_data, axis=0)).all()
If you would like to use your own (struct/union/whatever) data types in scan and reduction, define those types in the preamble and let PyCUDA know about them using this function:
pycuda.tools.
register_dtype
(dtype, name)¶dtype is a numpy.dtype()
.
Bogdan Opanchuk’s reikna offers a
variety of GPUbased algorithms (FFT, RNG, matrix multiplication) designed to work with
pycuda.gpuarray.GPUArray
objects.