GPU Arrays ========== .. module:: pycuda.gpuarray Vector Types ------------ .. class :: vec All of CUDA's supported vector types, such as `float3` and `long4` are available as :mod:`numpy` data types within this class. These :class:`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)`). The :class:`GPUArray` Array Class --------------------------------- .. class:: GPUArray(shape, dtype, *, allocator=None, order="C") A :class:`numpy.ndarray` work-alike that stores its data and performs its computations on the compute device. *shape* and *dtype* work exactly as in :mod:`numpy`. Arithmetic methods in :class:`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 :class:`int` representing the address of the newly allocated memory. Observe that both :func:`pycuda.driver.mem_alloc` and :meth:`pycuda.tools.DeviceMemoryPool.alloc` are a model of this interface. All arguments beyond *allocator* should be considered keyword-only. .. attribute :: gpudata The :class:`pycuda.driver.DeviceAllocation` instance created for the memory that backs this :class:`GPUArray`. .. attribute :: shape The tuple of lengths of each dimension in the array. .. attribute :: dtype The :class:`numpy.dtype` of the items in the GPU array. .. attribute :: size The number of meaningful entries in the array. Can also be computed by multiplying up the numbers in :attr:`shape`. .. attribute :: 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 :func:`pycuda.driver.mem_alloc_pitch`. .. attribute :: nbytes The size of the entire array in bytes. Computed as :attr:`size` times ``dtype.itemsize``. .. attribute :: strides Tuple of bytes to step in each dimension when traversing an array. .. attribute :: flags Return an object with attributes `c_contiguous`, `f_contiguous` and `forc`, which may be used to query contiguity properties in analogy to :attr:`numpy.ndarray.flags`. .. attribute :: ptr Return an :class:`int` reflecting the address in device memory where this array resides. .. versionadded: 2011.1 .. attribute :: __cuda_array_interface__ Return a `CUDA Array Interface `_ dict describing this array's data. .. method :: __len__() Returns the size of the leading dimension of *self*. .. warning :: This method existed in version 0.93 and below, but it returned the value of :attr:`size` instead of its current value. The change was made in order to match :mod:`numpy`. .. method :: reshape(shape, order="C") Returns an array containing the same data with a new shape. .. method :: ravel() Returns flattened array containing the same data. .. method :: 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. .. method :: squeeze(dtype=None) Returns a view of the array with dimensions of length 1 removed. .. versionadded: 2015.1.4 .. method :: set(ary) Transfer the contents the :class:`numpy.ndarray` object *ary* onto the device. *ary* must have the same dtype and size (not necessarily shape) as *self*. .. method :: set_async(ary, stream=None) Asynchronously transfer the contents the :class:`numpy.ndarray` object *ary* onto the device, optionally sequenced on *stream*. *ary* must have the same dtype and size (not necessarily shape) as *self*. .. method :: get(ary=None, pagelocked=False) Transfer the contents of *self* into *ary* or a newly allocated :mod:`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 page-locked. .. versionchanged:: 2015.2 *ary* with different shape was deprecated. .. method :: get_async(stream=None, ary=None) Transfer the contents of *self* into *ary* or a newly allocated :mod:`numpy.ndarray`. If *ary* is given, it must have the right size (not necessarily shape) and dtype. If it is not given, a *page-locked* array is newly allocated. .. method :: copy() .. versionadded :: 2013.1 .. method :: 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 :meth:`pycuda.driver.Function.prepared_timed_call`. .. method :: __add__(other) .. method :: __sub__(other) .. method :: __iadd__(other) .. method :: __isub__(other) .. method :: __neg__(other) .. method :: __mul__(other) .. method :: __div__(other) .. method :: __rdiv__(other) .. method :: __pow__(other) .. method :: __abs__() Return a :class:`GPUArray` containing the absolute value of each element of *self*. .. UNDOC reverse() .. method :: fill(scalar, stream=None) Fill the array with *scalar*. .. method :: astype(dtype, stream=None) Return *self*, cast to *dtype*. .. method :: any(stream=None, allocator=None) .. method :: all(stream=None, allocator=None) .. attribute :: real Return the real part of *self*, or *self* if it is real. .. versionadded:: 0.94 .. attribute :: imag Return the imaginary part of *self*, or *zeros_like(self)* if it is real. .. versionadded: 0.94 .. method :: conj(out=None) Return the complex conjugate of *self*, or *self* if it is real. If *out* is not given, a newly allocated :class:`GPUArray` will returned. Use *out=self* to get conjugate in-place. .. versionadded: 0.94 .. versionchanged:: 2020.1.1 add *out* parameter .. method :: conjugate(out=None) alias of :meth:`conj` .. versionadded:: 2020.1.1 .. method:: bind_to_texref(texref, allow_offset=False) Bind *self* to the :class:`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 :meth:`bind_to_texref_ext` instead of this method. .. method:: bind_to_texref_ext(texref, channels=1, allow_double_hack=False, allow_offset=False) Bind *self* to the :class:`pycuda.driver.TextureReference` *texref*. In addition, set the texture reference's format to match :attr:`dtype` and its channel count to *channels*. This routine also sets the texture reference's :data:`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. .. versionadded:: 0.93 .. highlight:: c As of this writing, CUDA textures do not natively support double-precision 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 texture my_tex; __global__ void f() { ... fp_tex1Dfetch(my_tex, threadIdx.x); ... } .. highlight:: python (This workaround was added in version 0.94.) Constructing :class:`GPUArray` Instances ---------------------------------------- .. function:: to_gpu(ary, allocator=None) Return a :class:`GPUArray` that is an exact copy of the :class:`numpy.ndarray` instance *ary*. See :class:`GPUArray` for the meaning of *allocator*. .. function:: to_gpu_async(ary, allocator=None, stream=None) Return a :class:`GPUArray` that is an exact copy of the :class:`numpy.ndarray` instance *ary*. The copy is done asynchronously, optionally sequenced into *stream*. See :class:`GPUArray` for the meaning of *allocator*. .. function:: empty(shape, dtype, *, allocator=None, order="C") A synonym for the :class:`GPUArray` constructor. .. function:: zeros(shape, dtype=np.float64, *, allocator=None, order="C") Same as :func:`empty`, but the :class:`GPUArray` is zero-initialized before being returned. .. function:: ones(shape, dtype=np.float64, *, allocator=None, order="C") Same as :func:`empty`, but the :class:`GPUArray` is one-initialized before being returned. .. function:: empty_like(other_ary, dtype=None, order="K") Make a new, uninitialized :class:`GPUArray` having the same properties as *other_ary*. The *dtype* and *order* attributes allow these aspects to be set independently of their values in *other_ary*. For *order*, "A" means retain Fortran-ordering if the input is Fortran-contiguous, otherwise use "C" ordering. The default, *order* or "K" tries to match the strides of *other_ary* as closely as possible. .. function:: zeros_like(other_ary, dtype=None, order="K") Make a new, zero-initialized :class:`GPUArray` having the same properties as *other_ary*. The *dtype* and *order* attributes allow these aspects to be set independently of their values in *other_ary*. For *order*, "A" means retain Fortran-ordering if the input is Fortran-contiguous, otherwise use "C" ordering. The default, *order* or "K" tries to match the strides of *other_ary* as closely as possible. .. function:: ones_like(other_ary, dtype=None, order="K") Make a new, ones-initialized :class:`GPUArray` having the same properties as *other_ary*. The *dtype* and *order* attributes allow these aspects to be set independently of their values in *other_ary*. For *order*, "A" means retain Fortran-ordering if the input is Fortran-contiguous, otherwise use "C" ordering. The default, *order* or "K" tries to match the strides of *other_ary* as closely as possible. .. versionadded: 2017.2 .. function:: arange(start, stop, step, dtype=None, stream=None) Create a :class:`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*. .. function:: take(a, indices, stream=None) Return the :class:`GPUArray` ``[a[indices[0]], ..., a[indices[n]]]``. For the moment, *a* must be a type that can be bound to a texture. .. function:: concatenate(arrays, axis=0, allocator=None) Join a sequence of arrays along an existing axis. .. function:: stack(arrays, axis=0, allocator=None) Join a sequence of arrays along a new axis. .. function:: logical_and(x1, x2, /, out=None, * allocator=None) Returns the elementwise logical AND values of *x1* and *x2*. .. function:: logical_or(x1, x2, /, out=None, * allocator=None) Returns the elementwise logical OR values of *x1* and *x2*. .. function:: logical_not(x, /, out=None, * allocator=None) Returns the elementwise logical NOT of *x*. Conditionals ^^^^^^^^^^^^ .. function:: 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) .. function:: maximum(a, b, out=None, stream=None) Return the elementwise maximum of *a* and *b*. (added in 0.94) .. function:: minimum(a, b, out=None, stream=None) Return the elementwise minimum of *a* and *b*. (added in 0.94) Reductions ^^^^^^^^^^ .. function:: sum(a, dtype=None, stream=None) .. function:: any(a, stream=None, allocator=None) .. function:: all(a, stream=None, allocator=None) .. function:: subset_sum(subset, a, dtype=None, stream=None) .. versionadded:: 2013.1 .. function:: dot(a, b, dtype=None, stream=None) .. function:: subset_dot(subset, a, b, dtype=None, stream=None) .. function:: max(a, stream=None) .. function:: min(a, stream=None) .. function:: subset_max(subset, a, stream=None) .. function:: subset_min(subset, a, stream=None) Elementwise Functions on :class:`GPUArray` Instances ----------------------------------------------------- .. module:: pycuda.cumath The :mod:`pycuda.cumath` module contains elementwise workalikes for the functions contained in :mod:`math`. Rounding and Absolute Value ^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. function:: fabs(array, *, out=None, stream=None) .. function:: ceil(array, *, out=None, stream=None) .. function:: floor(array, *, out=None, stream=None) Exponentials, Logarithms and Roots ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. function:: exp(array, *, out=None, stream=None) .. function:: log(array, *, out=None, stream=None) .. function:: log10(array, *, out=None, stream=None) .. function:: sqrt(array, *, out=None, stream=None) Trigonometric Functions ^^^^^^^^^^^^^^^^^^^^^^^ .. function:: sin(array, *, out=None, stream=None) .. function:: cos(array, *, out=None, stream=None) .. function:: tan(array, *, out=None, stream=None) .. function:: asin(array, *, out=None, stream=None) .. function:: acos(array, *, out=None, stream=None) .. function:: atan(array, *, out=None, stream=None) Hyperbolic Functions ^^^^^^^^^^^^^^^^^^^^ .. function:: sinh(array, *, out=None, stream=None) .. function:: cosh(array, *, out=None, stream=None) .. function:: tanh(array, *, out=None, stream=None) Floating Point Decomposition and Assembly ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. function:: fmod(arg, mod, stream=None) Return the floating point remainder of the division `arg/mod`, for each element in `arg` and `mod`. .. function:: frexp(arg, stream=None) Return a tuple `(significands, exponents)` such that `arg == significand * 2**exponent`. .. function:: 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`. .. function:: modf(arg, stream=None) Return a tuple `(fracpart, intpart)` of arrays containing the integer and fractional parts of `arg`. Generating Arrays of Random Numbers ----------------------------------- .. module:: pycuda.curandom .. function:: 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 MD5-based generator and doesn't even attempt to cache generated code. If you're interested in a non-toy random number generator, use the CURAND-based 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 n-dimensional points is generated by a deterministic algorithm designed to fill an n-dimensional space evenly. Quasirandom numbers are more expensive to generate. .. function:: get_curand_version() Obtain the version of CURAND against which PyCUDA was compiled. Returns a 3-tuple of integers as *(major, minor, revision)*. .. function:: seed_getter_uniform(N) Return an :class:`GPUArray` filled with one random `int32` repeated `N` times which can be used as a seed for XORWOW generator. .. function:: seed_getter_unique(N) Return an :class:`GPUArray` filled with `N` random `int32` which can be used as a seed for XORWOW generator. .. class:: XORWOWRandomNumberGenerator(seed_getter=None, offset=0) :arg seed_getter: a function that, given an integer count, will yield an `int32` :class:`GPUArray` of seeds. :arg offset: Starting index into the XORWOW sequence, given seed. Provides pseudorandom numbers. Generates sequences with period at least :math:`2^190`. CUDA 3.2 and above. .. versionadded:: 2011.1 .. method:: fill_uniform(data, stream=None) Fills in :class:`GPUArray` *data* with uniformly distributed pseudorandom values. .. method:: gen_uniform(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with uniformly distributed pseudorandom values, and returns newly created object. .. method:: fill_normal(data, stream=None) Fills in :class:`GPUArray` *data* with normally distributed pseudorandom values. .. method:: gen_normal(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with normally distributed pseudorandom values, and returns newly created object. .. method:: fill_log_normal(data, mean, stddev, stream=None) Fills in :class:`GPUArray` *data* with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*. CUDA 4.0 and above. .. versionadded:: 2012.2 .. method:: gen_log_normal(shape, dtype, mean, stddev, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*, and returns newly created object. CUDA 4.0 and above. .. versionadded:: 2012.2 .. method:: fill_poisson(data, lambda_value=None, stream=None) Fills in :class:`GPUArray` *data* with Poisson distributed pseudorandom values. If *lambda_value* is not None, it is used as lambda, and *data* must be of type 32-bit unsigned int. If *lambda_value* is None, the lambda value is read from each *data* array element (similarly to numpy.random.poisson), and the array is overwritten by the pseudorandom values. *data* must be of type 32-bit unsigned int, 32 or 64-bit float. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: gen_poisson(shape, dtype, lambda_value, stream=None) Creates object of :class:`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 32-bit unsigned int. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: 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. .. method:: call_skip_ahead_array(i, stream=None) Accepts array i of integer values, telling each generator how many values to skip. .. method:: call_skip_ahead_sequence(i, stream=None) Forces all generators to skip i subsequences. Is equivalent to generating i * :math:`2^67` values and discarding results, but is much faster. .. method:: call_skip_ahead_sequence_array(i, stream=None) Accepts array i of integer values, telling each generator how many subsequences to skip. .. class:: MRG32k3aRandomNumberGenerator(seed_getter=None, offset=0) :arg seed_getter: a function that, given an integer count, will yield an `int32` :class:`GPUArray` of seeds. :arg offset: Starting index into the XORWOW sequence, given seed. Provides pseudorandom numbers. Generates sequences with period at least :math:`2^190`. CUDA 4.1 and above. .. versionadded:: 2013.1 .. method:: fill_uniform(data, stream=None) Fills in :class:`GPUArray` *data* with uniformly distributed pseudorandom values. .. method:: gen_uniform(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with uniformly distributed pseudorandom values, and returns newly created object. .. method:: fill_normal(data, stream=None) Fills in :class:`GPUArray` *data* with normally distributed pseudorandom values. .. method:: gen_normal(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with normally distributed pseudorandom values, and returns newly created object. .. method:: fill_log_normal(data, mean, stddev, stream=None) Fills in :class:`GPUArray` *data* with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*. .. method:: gen_log_normal(shape, dtype, mean, stddev, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*, and returns newly created object. .. method:: fill_poisson(data, lambda_value, stream=None) Fills in :class:`GPUArray` *data* with Poisson distributed pseudorandom values. If *lambda_value* is not None, it is used as lambda, and *data* must be of type 32-bit unsigned int. If *lambda_value* is None, the lambda value is read from each *data* array element (similarly to numpy.random.poisson), and the array is overwritten by the pseudorandom values. *data* must be of type 32-bit unsigned int, 32 or 64-bit float. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: gen_poisson(shape, dtype, lambda_value, stream=None) Creates object of :class:`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 32-bit unsigned int. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: 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. .. method:: call_skip_ahead_array(i, stream=None) Accepts array i of integer values, telling each generator how many values to skip. .. method:: call_skip_ahead_sequence(i, stream=None) Forces all generators to skip i subsequences. Is equivalent to generating i * :math:`2^67` values and discarding results, but is much faster. .. method:: call_skip_ahead_sequence_array(i, stream=None) Accepts array i of integer values, telling each generator how many subsequences to skip. .. function:: generate_direction_vectors(count, direction=direction_vector_set.VECTOR_32) Return an :class:`GPUArray` `count` filled with direction vectors used to initialize Sobol generators. .. function:: generate_scramble_constants32(count) Return a :class:`GPUArray` filled with `count' 32-bit unsigned integer numbers used to initialize :class:`ScrambledSobol32RandomNumberGenerator` .. function:: generate_scramble_constants64(count) Return a :class:`GPUArray` filled with `count' 64-bit unsigned integer numbers used to initialize :class:`ScrambledSobol64RandomNumberGenerator` .. class:: Sobol32RandomNumberGenerator(dir_vector=None, offset=0) :arg dir_vector: a :class:`GPUArray` of 32-element `int32` vectors which are used to initialize quasirandom generator; it must contain one vector for each initialized generator :arg offset: Starting index into the Sobol32 sequence, given direction vector. Provides quasirandom numbers. Generates sequences with period of :math:`2^32`. CUDA 3.2 and above. .. versionadded:: 2011.1 .. method:: fill_uniform(data, stream=None) Fills in :class:`GPUArray` *data* with uniformly distributed quasirandom values. .. method:: gen_uniform(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with uniformly distributed pseudorandom values, and returns newly created object. .. method:: fill_normal(data, stream=None) Fills in :class:`GPUArray` *data* with normally distributed quasirandom values. .. method:: gen_normal(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with normally distributed pseudorandom values, and returns newly created object. .. method:: fill_log_normal(data, mean, stddev, stream=None) Fills in :class:`GPUArray` *data* with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*. CUDA 4.0 and above. .. versionadded:: 2012.2 .. method:: gen_log_normal(shape, dtype, mean, stddev, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*, and returns newly created object. CUDA 4.0 and above. .. versionadded:: 2012.2 .. method:: fill_poisson(data, lambda_value, stream=None) Fills in :class:`GPUArray` *data* with Poisson distributed pseudorandom values. If *lambda_value* is not None, it is used as lambda, and *data* must be of type 32-bit unsigned int. If *lambda_value* is None, the lambda value is read from each *data* array element (similarly to numpy.random.poisson), and the array is overwritten by the pseudorandom values. *data* must be of type 32-bit unsigned int, 32 or 64-bit float. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: gen_poisson(shape, dtype, lambda_value, stream=None) Creates object of :class:`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 32-bit unsigned int. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: 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. .. method:: call_skip_ahead_array(i, stream=None) Accepts array i of integer values, telling each generator how many values to skip. .. class:: ScrambledSobol32RandomNumberGenerator(dir_vector=None, scramble_vector=None, offset=0) :arg dir_vector: a :class:`GPUArray` of 32-element `uint32` vectors which are used to initialize quasirandom generator; it must contain one vector for each initialized generator :arg scramble_vector: a :class:`GPUArray` of `uint32` elements which are used to initialize quasirandom generator; it must contain one number for each initialized generator :arg offset: Starting index into the Sobol32 sequence, given direction vector. Provides quasirandom numbers. Generates sequences with period of :math:`2^32`. CUDA 4.0 and above. .. versionadded:: 2011.1 .. method:: fill_uniform(data, stream=None) Fills in :class:`GPUArray` *data* with uniformly distributed quasirandom values. .. method:: gen_uniform(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with uniformly distributed pseudorandom values, and returns newly created object. .. method:: fill_normal(data, stream=None) Fills in :class:`GPUArray` *data* with normally distributed quasirandom values. .. method:: gen_normal(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with normally distributed pseudorandom values, and returns newly created object. .. method:: fill_log_normal(data, mean, stddev, stream=None) Fills in :class:`GPUArray` *data* with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*. CUDA 4.0 and above. .. versionadded:: 2012.2 .. method:: gen_log_normal(shape, dtype, mean, stddev, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*, and returns newly created object. CUDA 4.0 and above. .. versionadded:: 2012.2 .. method:: fill_poisson(data, lambda_value, stream=None) Fills in :class:`GPUArray` *data* with Poisson distributed pseudorandom values. If *lambda_value* is not None, it is used as lambda, and *data* must be of type 32-bit unsigned int. If *lambda_value* is None, the lambda value is read from each *data* array element (similarly to numpy.random.poisson), and the array is overwritten by the pseudorandom values. *data* must be of type 32-bit unsigned int, 32 or 64-bit float. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: gen_poisson(shape, dtype, lambda_value, stream=None) Creates object of :class:`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 32-bit unsigned int. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: 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. .. method:: call_skip_ahead_array(i, stream=None) Accepts array i of integer values, telling each generator how many values to skip. .. class:: Sobol64RandomNumberGenerator(dir_vector=None, offset=0) :arg dir_vector: a :class:`GPUArray` of 64-element `uint64` vectors which are used to initialize quasirandom generator; it must contain one vector for each initialized generator :arg offset: Starting index into the Sobol64 sequence, given direction vector. Provides quasirandom numbers. Generates sequences with period of :math:`2^64`. CUDA 4.0 and above. .. versionadded:: 2011.1 .. method:: fill_uniform(data, stream=None) Fills in :class:`GPUArray` *data* with uniformly distributed quasirandom values. .. method:: gen_uniform(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with uniformly distributed pseudorandom values, and returns newly created object. .. method:: fill_normal(data, stream=None) Fills in :class:`GPUArray` *data* with normally distributed quasirandom values. .. method:: gen_normal(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with normally distributed pseudorandom values, and returns newly created object. .. method:: fill_log_normal(data, mean, stddev, stream=None) Fills in :class:`GPUArray` *data* with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*. CUDA 4.0 and above. .. versionadded:: 2012.2 .. method:: gen_log_normal(shape, dtype, mean, stddev, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*, and returns newly created object. CUDA 4.0 and above. .. versionadded:: 2012.2 .. method:: fill_poisson(data, lambda_value, stream=None) Fills in :class:`GPUArray` *data* with Poisson distributed pseudorandom values. If *lambda_value* is not None, it is used as lambda, and *data* must be of type 32-bit unsigned int. If *lambda_value* is None, the lambda value is read from each *data* array element (similarly to numpy.random.poisson), and the array is overwritten by the pseudorandom values. *data* must be of type 32-bit unsigned int, 32 or 64-bit float. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: gen_poisson(shape, dtype, lambda_value, stream=None) Creates object of :class:`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 32-bit unsigned int. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: 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. .. method:: call_skip_ahead_array(i, stream=None) Accepts array i of integer values, telling each generator how many values to skip. .. class:: ScrambledSobol64RandomNumberGenerator(dir_vector=None, scramble_vector=None, offset=0) :arg dir_vector: a :class:`GPUArray` of 64-element `uint64` vectors which are used to initialize quasirandom generator; it must contain one vector for each initialized generator :arg scramble_vector: a :class:`GPUArray` of `uint64` vectors which are used to initialize quasirandom generator; it must contain one vector for each initialized generator :arg offset: Starting index into the ScrambledSobol64 sequence, given direction vector. Provides quasirandom numbers. Generates sequences with period of :math:`2^64`. CUDA 4.0 and above. .. versionadded:: 2011.1 .. method:: fill_uniform(data, stream=None) Fills in :class:`GPUArray` *data* with uniformly distributed quasirandom values. .. method:: gen_uniform(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with uniformly distributed pseudorandom values, and returns newly created object. .. method:: fill_normal(data, stream=None) Fills in :class:`GPUArray` *data* with normally distributed quasirandom values. .. method:: gen_normal(shape, dtype, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with normally distributed pseudorandom values, and returns newly created object. .. method:: fill_log_normal(data, mean, stddev, stream=None) Fills in :class:`GPUArray` *data* with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*. CUDA 4.0 and above. .. versionadded:: 2012.2 .. method:: gen_log_normal(shape, dtype, mean, stddev, stream=None) Creates object of :class:`GPUArray` with given *shape* and *dtype*, fills it in with log-normally distributed pseudorandom values with mean *mean* and standard deviation *stddev*, and returns newly created object. CUDA 4.0 and above. .. versionadded:: 2012.2 .. method:: fill_poisson(data, lambda_value, stream=None) Fills in :class:`GPUArray` *data* with Poisson distributed pseudorandom values. If *lambda_value* is not None, it is used as lambda, and *data* must be of type 32-bit unsigned int. If *lambda_value* is None, the lambda value is read from each *data* array element (similarly to numpy.random.poisson), and the array is overwritten by the pseudorandom values. *data* must be of type 32-bit unsigned int, 32 or 64-bit float. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: gen_poisson(shape, dtype, lambda_value, stream=None) Creates object of :class:`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 32-bit unsigned int. CUDA 5.0 and above. .. versionadded:: 2013.1 .. method:: 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. .. method:: call_skip_ahead_array(i, stream=None) Accepts array i of integer values, telling each generator how many values to skip. Single-pass Custom Expression Evaluation ---------------------------------------- .. module:: pycuda.elementwise Evaluating involved expressions on :class:`GPUArray` instances can be somewhat inefficient, because a new temporary is created for each intermediate result. The functionality in the module :mod:`pycuda.elementwise` contains tools to help generate kernels that evaluate multi-stage expressions on one or several operands in a single pass. .. class:: 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 :class:`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*. .. method:: __call__(*args, range=None, slice=None) Invoke the generated scalar kernel. The arguments may either be scalars or :class:`GPUArray` instances. If *range* is given, it must be a :class:`slice` object and specifies the range of indices *i* for which the *operation* is carried out. If *slice* is given, it must be a :class:`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 :class:`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()) < 1e-5 (You can find this example as :file:`examples/demo_elementwise.py` in the PyCuda distribution.) Custom Reductions ----------------- .. module:: pycuda.reduction .. class:: 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. *reduce_expr* must be associative. *dtype_out* specifies the :class:`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 :class:`pycuda.compiler.SourceModule`. *preamble* is specified as a string of code. .. method:: __call__(*args, stream=None, out=None) Invoke the generated reduction kernel. The arguments may either be scalars or :class:`GPUArray` instances. The reduction will be done on each entry of the first vector argument. If *stream* is given, it must be a :class:`pycuda.driver.Stream` object, where the execution will be serialized. With *out* the resulting single-entry :class:`GPUArray` can be specified. Because offsets are supported one can store results anywhere (e.g. out=a[3]). 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() Or by specifying the output:: from pycuda.curandom import rand as curand a = curand((10, 200), dtype=np.float32) red = ReductionKernel(np.float32, neutral=0, reduce_expr="a+b", arguments="float *in") a_sum = gpuarray.empty(10, dtype=np.float32) for i in range(10): red(a[i], out=a_sum[i]) assert(np.allclose(a_sum.get(), a.get().sum(axis=1))) Parallel Scan / Prefix Sum -------------------------- .. module:: pycuda.scan .. class:: 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. .. method:: __call__(self, input_ary, output_ary=None, allocator=None, queue=None) .. class:: InclusiveScanKernel(dtype, scan_expr, neutral=None, name_prefix="scan", options=[], preamble="", devices=None) Works like :class:`ExclusiveScanKernel`. Unlike the exclusive case, *neutral* is not required. Here's a usage example:: knl = InclusiveScanKernel(np.int32, "a+b") n = 2**20-2**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() Custom data types in Reduction and Scan --------------------------------------- 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: .. function:: pycuda.tools.register_dtype(dtype, name) *dtype* is a :func:`numpy.dtype`. .. versionadded: 2011.2 GPGPU Algorithms ---------------- Bogdan Opanchuk's `reikna `_ offers a variety of GPU-based algorithms (FFT, RNG, matrix multiplication) designed to work with :class:`pycuda.gpuarray.GPUArray` objects.