- Suppose that you use nvcc to compile a single .cu file containing both host and kernel code into an EXE file, and also into a PTX file. Which file will contain the host functions, and which file will contain the GPU code?
- Why do we have to destroy a context if we are using the CUDA Driver API?
- At the beginning of this chapter when we first saw how to use Ctypes, notice that we had to typecast the floating point value 3.14 to a Ctypes c_double object in a call to printf before it would work. Yet we can see many working cases of not typecasting to Ctypes in this chapter. Why do you think printf is an exception here?
- Suppose you want to add functionality to our Python CUDA Driver interface module to support CUDA streams. How would you represent a single stream object in Ctypes?
- Why do we use extern "C" for functions in mandelbrot.cu?
- Look at mandelbrot_driver...
Hands-On GPU Programming with Python and CUDA
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Hands-On GPU Programming with Python and CUDA
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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.
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Current Title:
Hands-On GPU Programming with Python and CUDA
Table of Contents (15 chapters)
Preface
Free Chapter
Why GPU Programming?
Setting Up Your GPU Programming Environment
Getting Started with PyCUDA
Kernels, Threads, Blocks, and Grids
Streams, Events, Contexts, and Concurrency
Debugging and Profiling Your CUDA Code
Using the CUDA Libraries with Scikit-CUDA
The CUDA Device Function Libraries and Thrust
Implementation of a Deep Neural Network
Working with Compiled GPU Code
Performance Optimization in CUDA
Where to Go from Here
Assessment
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Customer Reviews