Hands-On GPU Programming with Python and CUDA: Boost your application's performance and productivity with CUDA: Explore high-performance parallel computing with CUDA (Paperback)

Dr. Brian Tuomanen

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商品描述

Build real-world applications by writing effective GPU code, CUDA kernels, and device functions with the latest features of Python 3.7, CUDA 9 and CUDA 10

Key Features

  • Expand your background in GPU programming PyCUDA, scikit-cuda, and Nsight
  • Effectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolver
  • Apply GPU programming to modern data science applications

Book Description

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.

What you will learn

  • Launch GPU code directly from Python
  • Write effective and efficient GPU kernels and device functions
  • Use libraries such as cuFFT, cuBLAS, and cuSolver
  • Debug and profile your code with Nsight and Visual Profiler
  • Apply GPU programming to datascience problems
  • Build a GPU-based deep neuralnetwork from scratch
  • Explore advanced GPU hardware features, such as warp shuffling

Who this book is for

Hands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.

Table of Contents

  1. Why GPU Programming?
  2. Setting Up Your GPU Programming Environment
  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 Draft complete
  8. The CUDA Device Function Libraries and Thrust
  9. Implementing a Deep Neural Network
  10. Working with Compiled GPU Code
  11. Performance Optimization in CUDA
  12. Where to Go from Here

商品描述(中文翻譯)

使用Python 3.7、CUDA 9和CUDA 10的最新功能,通過撰寫有效的GPU代碼、CUDA核心和設備函數,構建真實世界應用程式。

主要特點:

- 通過PyCUDA、scikit-cuda和Nsight擴展您在GPU編程方面的背景知識。
- 有效使用CUDA庫,如cuBLAS、cuFFT和cuSolver。
- 將GPU編程應用於現代數據科學應用。

書籍描述:

《使用Python和CUDA進行GPU編程實踐》立即開始:您將首先學習如何應用Amdahl定律,使用代碼分析器來識別Python代碼中的瓶頸,並設置適當的GPU編程環境。然後,您將了解如何查詢GPU的功能並將數組數據從GPU的內存中複製進行操作。

隨著您閱讀本書,您將直接將代碼部署到GPU上,並使用CUDA C編寫完整的GPU核心和設備函數。您將有效地進行GPU代碼分析並使用Nsight IDE進行全面的測試和調試。接下來,您將探索一些較為著名的NVIDIA庫,如cuFFT和cuBLAS。

在建立了堅實的基礎之後,您現在可以應用您新獲得的知識從頭開始開發基於GPU的深度神經網絡。然後,您將探索高級主題,如warp shuffling、動態並行性和PTX組合語言。在最後一章中,您將看到一些與GPU編程相關的主題和應用,包括人工智能、圖形和區塊鏈。

通過閱讀本書,您將能夠將GPU編程應用於與數據科學和高性能計算相關的問題。

您將學到什麼:

- 直接從Python啟動GPU代碼。
- 撰寫有效且高效的GPU核心和設備函數。
- 使用庫,如cuFFT、cuBLAS和cuSolver。
- 使用Nsight和Visual Profiler調試和分析代碼。
- 將GPU編程應用於數據科學問題。
- 從頭開始構建基於GPU的深度神經網絡。
- 探索高級GPU硬件功能,如warp shuffling。

本書適合對於改進性能使用Python代碼學習基礎GPU編程的開發人員和數據科學家。您應該對大學一年級工程數學和物理有一定的了解,並且對Python以及C、C++、Go或Java等基於C的編程語言有一些經驗。

目錄:

1. 為什麼需要GPU編程?
2. 設置GPU編程環境
3. 開始使用PyCUDA
4. 核心、線程、塊和網格
5. 流、事件、上下文和並行性
6. 調試和分析CUDA代碼
7. 使用Scikit-CUDA的CUDA庫(草稿)
8. CUDA設備函數庫和Thrust
9. 實現深度神經網絡
10. 使用編譯的GPU代碼
11. CUDA中的性能優化
12. 從這裡開始前進