Book Image

Hands-On GPU Programming with Python and CUDA

By : Dr. Brian Tuomanen
Book Image

Hands-On GPU Programming with Python and CUDA

By: Dr. Brian Tuomanen

Overview of this book

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. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory. As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS. With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain. By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing.
Table of Contents (15 chapters)

Chapter 3, Getting Started with PyCUDA

  1. Yes.
  2. Memory transfers between host/device, and compilation time.
  3. You can, but this will vary depending on your GPU and CPU setup.
  4. Do this using the C ? operator for both the point-wise and reduce operations.
  5. If a gpuarray object goes out of scope its destructor is called, which will deallocate (free) the memory it represents on the GPU automatically.
  6. ReductionKernel may perform superfluous operations, which may be necessary depending on how the underlying GPU code is structured. A neutral element will ensure that no values are altered as a result of these superfluous operations.
  7. We should set neutral to the smallest possible value of a signed 32-bit integer.