3.5. Supported Python features in CUDA Python

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.

3.5.1. Language Execution Model

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. Constructs

The following Python constructs are not supported:

  • Exception handling (try .. except, try .. finally)
  • Context management (the with statement)
  • Comprehensions (either list, dict, set or generator comprehensions)
  • Generator (any yield statements)

The raise and assert statements are supported. See nopython language support.

3.5.2. Built-in types

The following built-in types support are inherited from CPU nopython mode.

  • int
  • float
  • complex
  • bool
  • None
  • tuple

See nopython built-in types.

3.5.3. Built-in functions

The following built-in functions are supported:

3.5.4. Standard library modules

3.5.5. Numpy support

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:

  • accessing ndarray attributes .shape, .strides, .ndim, .size, etc..
  • scalar ufuncs that have equivalents in the math module; i.e. np.sin(x[0]), where x is a 1D array.
  • indexing and slicing works.

Unsupported numpy features:

  • array creation APIs.
  • array methods.
  • functions that returns a new array.