Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment.

Updated to cover the latest Python 3 features, custom TensorFlow modules, and ray tracing, this second edition is your guide to building GPU-accelerated high-performing applications Key Features * Get to grips with graphics processing unit (GPU) programming tools such as PyCUDA, scikit-cuda, and Nsight * Explore CUDA libraries such as cuB...

Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book.

Hands-On GPU Programming with CUDA C and Python 3.x, Second Edition, published by Packt.

It begins by introducing GPU computing and explaining the GPU architecture and programming models. You will learn, by example, how to perform GPU programming with Python, and look at using integrations such as PyCUDA, PyOpenCL, CuPy, and Numba with Anaconda for various tasks such as machine learning and data mining.

Nov 27, 2018 · Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and...

In this section, we will explore how to compile and run CUDA code in Google Colab, leveraging the power of NVIDIA GPUs. This hands-on GPU programming with Python allows you to execute high-performance CUDA applications directly in your browser without the need for local GPU resources. Setting Up Your Environment