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)

Using cuSolver from Scikit-CUDA

We will now look at how we can use cuSolver from Scikit-CUDA's linalg submodule. Again, this provides a high-level interface for both cuBLAS and cuSolver, so we don't have to get caught up in the small details.

As we noted in the introduction, cuSolver is a library that's used for performing more advanced linear algebra operations than cuBLAS, such as the Singular Value Decomposition, LU/QR/Cholesky factorization, and eigenvalue computations. Since cuSolver, like cuBLAS and cuFFT, is another vast library, we will only take the time to look at one of the most fundamental operations in data science and machine learning—SVD.

Please refer to NVIDIA's official documentation on cuSOLVER if you would like further information on this library: https://docs.NVIDIA.com/cuda/cusolver/index.html.
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