3.1. Overview

Numba supports CUDA GPU programming by directly compiling a restricted subset of Python code into CUDA kernels and device functions following the CUDA execution model. Kernels written in Numba appear to have direct access to NumPy arrays. NumPy arrays are transferred between the CPU and the GPU automatically.

3.1.1. Terminology

Several important terms in the topic of CUDA programming are listed here:

• host: the CPU
• device: the GPU
• host memory: the system main memory
• device memory: onboard memory on a GPU card
• kernels: a GPU function launched by the host and executed on the device
• device function: a GPU function executed on the device which can only be called from the device (i.e. from a kernel or another device function)

3.1.2. Programming model

Most CUDA programming facilities exposed by Numba map directly to the CUDA C language offered by NVidia. Therefore, it is recommended you read the official CUDA C programming guide.

3.1.3. Requirements

3.1.3.1. Supported GPUs

Numba supports CUDA-enabled GPU with compute capability 2.0 or above with an up-to-data Nvidia driver.

3.1.3.2. Software

You will need the CUDA toolkit installed. If you are using Conda, just type:

$ conda install cudatoolkit

3.1.4. Missing CUDA Features

Numba does not implement all features of CUDA, yet. Some missing features are listed below:

• dynamic parallelism
• texture memory