This page lists the Python features supported in the CUDA Python. This includes
all kernel and device functions compiled with @cuda.jit
and other higher
level Numba decorators that targets the CUDA GPU.
CUDA Python maps directly to the single-instruction multiple-thread execution (SIMT) model of CUDA. Each instruction is implicitly executed by multiple threads in parallel. With this execution model, array expressions are less useful because we don’t want multiple threads to perform the same task. Instead, we want threads to perform a task in a cooperative fashion.
For details please consult the CUDA Programming Guide.
The following Python constructs are not supported:
try .. except
, try .. finally
)with
statement)yield
statements)The raise
and assert
statements are supported.
See nopython language support.
The following built-in types support are inherited from CPU nopython mode.
The following built-in functions are supported:
math
¶The following functions from the math
module are supported:
math.acos()
math.asin()
math.atan()
math.arctan()
math.acosh()
math.asinh()
math.atanh()
math.cos()
math.sin()
math.tan()
math.hypot()
math.cosh()
math.sinh()
math.tanh()
math.atan2()
math.erf()
math.erfc()
math.exp()
math.expm1()
math.fabs()
math.gamma()
math.lgamma()
math.log()
math.log10()
math.log1p()
math.sqrt()
math.pow()
math.ceil()
math.floor()
math.copysign()
math.fmod()
math.isnan()
math.isinf()
operator
¶The following functions from the operator
module are supported:
operator.add()
operator.and_()
operator.eq()
operator.floordiv()
operator.ge()
operator.gt()
operator.iadd()
operator.iand()
operator.ifloordiv()
operator.ilshift()
operator.imod()
operator.imul()
operator.invert()
operator.ior()
operator.ipow()
operator.irshift()
operator.isub()
operator.itruediv()
operator.ixor()
operator.le()
operator.lshift()
operator.lt()
operator.mod()
operator.mul()
operator.ne()
operator.neg()
operator.not_()
operator.or_()
operator.pos()
operator.pow()
operator.rshift()
operator.sub()
operator.truediv()
operator.xor()
Due to the CUDA programming model, dynamic memory allocation inside a kernel is inefficient and is often not needed. Numba disallows any memory allocating features. This disables a large number of NumPy APIs. For best performance, users should write code such that each thread is dealing with a single element at a time.
Supported numpy features:
np.sin(x[0])
, where x is a 1D array.Unsupported numpy features: