NVIDIA’s CUDA Python provides a driver and runtime API for existing toolkits and libraries to simplify GPU-based accelerated processing. Python is one of the most popular programming languages for science, engineering, data analytics, and deep learning applications. However, as an interpreted language, it’s been considered too slow for high-performance computing.
Numba—a Python compiler from Anaconda that can compile Python code for execution on CUDA®-capable GPUs—provides Python developers with an easy entry into GPU-accelerated computing and for using increasingly sophisticated CUDA code with a minimum of new syntax and jargon. With CUDA Python and Numba, you get the best of both worlds: rapid iterative development with Python combined with the speed of a compiled language targeting both CPUs and NVIDIA GPUs.
Set Up CUDA Python
To run CUDA Python, you’ll need the CUDA Toolkit installed on a system with CUDA-capable GPUs. Use this guide to install CUDA. If you don’t have a CUDA-capable GPU, you can access one of the thousands of GPUs available from cloud service providers, including Amazon AWS, Microsoft Azure, and IBM SoftLayer. The NVIDIA-maintained CUDA Amazon Machine Image (AMI) on AWS, for example, comes pre-installed with CUDA and is available for use today.
To get started with Numba, the first step is to download and install the Anaconda Python distribution that includes many popular packages (Numpy, SciPy, Matplotlib, iPython, etc.) and “conda,” a powerful package manager. Once you have Anaconda installed, install the required CUDA packages by typing conda install numba cudatoolkit pyculib.
Getting Started Blogs
Numba: High-Performance Python with CUDA Acceleration
Numba provides Python developers with an easy entry into GPU-accelerated computing and a path for using increasingly sophisticated CUDA code with a minimum of new syntax and jargon.
Numba runs inside the standard Python interpreter, so you can write CUDA kernels directly in Python syntax and execute them on the GPU. Dive deeper into several aspects of using Numba on the GPU that are often overlooked.
GPU-Accelerated Graph Analytics in Python with Numba
Numba provides Python developers with an easy entry into GPU-accelerated computing and a path for using increasingly sophisticated CUDA code with a minimum of new syntax and jargon.
