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Hands-On GPU Programming with Python and CUDA

You're reading from   Hands-On GPU Programming with Python and CUDA Explore high-performance parallel computing with CUDA

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788993913
Length 310 pages
Edition 1st Edition
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Author (1):
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Dr. Brian Tuomanen Dr. Brian Tuomanen
Author Profile Icon Dr. Brian Tuomanen
Dr. Brian Tuomanen
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Table of Contents (15) Chapters Close

Preface 1. Why GPU Programming? 2. Setting Up Your GPU Programming Environment FREE CHAPTER 3. Getting Started with PyCUDA 4. Kernels, Threads, Blocks, and Grids 5. Streams, Events, Contexts, and Concurrency 6. Debugging and Profiling Your CUDA Code 7. Using the CUDA Libraries with Scikit-CUDA 8. The CUDA Device Function Libraries and Thrust 9. Implementation of a Deep Neural Network 10. Working with Compiled GPU Code 11. Performance Optimization in CUDA 12. Where to Go from Here 13. Assessment 14. Other Books You May Enjoy

Basic linear algebra with cuBLAS

We will start this chapter by learning how to use Scikit-CUDA's cuBLAS wrappers. Let's spend a moment discussing BLAS. BLAS (Basic Linear Algebra Subroutines) is a specification for a basic linear algebra library that was first standardized in the 1970s. BLAS functions are broken down into several categories, which are referred to as levels.

Level 1 BLAS functions consist of operations purely on vectors—vector-vector addition and scaling (also known as ax+y operations, or AXPY), dot products, and norms. Level 2 BLAS functions consist of general matrix-vector operations (GEMV), such as matrix multiplication of a vector, while level 3 BLAS functions consist of "general matrix-matrix" (GEMM) operations, such as matrix-matrix multiplication. Originally, these libraries were written entirely in FORTRAN in the 1970s, so you...

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