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)

Compiling and launching pure PTX code

We have just seen how to call a pure-C function from Ctypes. In some ways, this may seem a little inelegant, as our binary file must contain both host code as well as the compiled GPU code, which may seem cumbersome. Can we just use pure, compiled GPU code and then launch it appropriately onto the GPU without writing a C wrapper each and every time? Fortunately, we can.

The NVCC compiler compiles CUDA-C into PTX (Parallel Thread Execution), which is an interpreted pseudo-assembly language that is compatible across NVIDIA 's various GPU architectures. Whenever you compile a program that uses a CUDA kernel with NVCC into an executable EXE, DLL, .so, or ELF file, there will be PTX code for that kernel contained within the file. We can also directly compile a file with the extension PTX, which will contain only the compiled GPU kernels from...