We started out in this chapter by seeing how printf can be used within a CUDA kernel to output data from individual threads; we saw in particular how useful this can be for debugging code. We then covered some of the gaps in our knowledge in CUDA-C, so that we can write full test programs that we can compile into proper executable binary files: there is a lot of overhead here that was hidden from us before that we have to be meticulous about. Next, we saw how to create and compile a project in the Nsight IDE and how to use it for debugging. We saw how to stop at any breakpoint we set in a CUDA kernel and switch between individual threads to see the different local variables. We also used the Nsight debugger to learn about the warp lockstep property and why it is important to avoid branch divergence in CUDA kernels. Finally, we had a very brief overview of the NVIDIA command...
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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