Librería Portfolio

Key Features

Provides in-depth, comprehensive coverage of GPUs with MATLAB, including the parallel computing toolbox and built-in features for other MATLAB toolboxes
Explains how to accelerate computationally heavy applications in MATLAB without the need to re-write them in another language
Presents case studies illustrating key concepts across multiple fields
Includes source code, sample datasets, and lecture slides
Description

GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development.
Readership

Scientists working in MATLAB who wish to leverage GPUs; high performance computing engineers wishing to incorporate MATLAB; students studying these topics.



Dedication
About the Authors
Foreword
Preface
Chapter 1: Introduction
Abstract
1.1 Parallel Programming
1.2 GPU Programming
1.3 CUDA Architecture
1.4 Why GPU Programming in MATLAB? When to Use GPU Programming?
1.5 Our Approach: Organization of the Book
1.6 Chapter Review
Chapter 2: Getting started
Abstract
Chapter Objectives
2.1 Hardware Requirements
2.2 Software Requirements
2.2.1 NVIDIA CUDA Toolkit
2.3 Chapter Review
Chapter 3: Parallel Computing Toolbox
Abstract
3.1 Product Description and Objectives
3.2 Parallel for-Loops (parfor)
3.3 Single Program Multiple Data (spmd)
3.4 Distributed and Codistributed Arrays
3.5 Interactive Parallel Development (pmode)
3.6 GPU Computing
3.7 Clusters and Job Scheduling
3.8 Chapter Review
Chapter 4: Introduction to GPU programming in MATLAB
Abstract
4.1 GPU Programming Features in MATLAB
4.2 GPU Arrays
4.3 Built-in MATLAB Functions for GPUs
4.4 Element-Wise MATLAB Code on GPUs
4.5 Chapter Review
Chapter 5: GPU programming on MATLAB toolboxes
Abstract
5.1 Communications System Toolbox
5.2 Image Processing Toolbox
5.3 Neural Network Toolbox
5.4 Phased Array System Toolbox
5.5 Signal Processing Toolbox
5.6 Statistics and Machine Learning Toolbox
5.7 Chapter Review
Chapter 6: Multiple GPUs
Abstract
6.1 Identify and Run Code on a Specific GPU Device
6.2 Examples Using Multiple GPUs
6.3 Chapter Review
Chapter 7: Run CUDA or PTX code
Abstract
7.1 A Brief Introduction to CUDA C
7.2 Steps to Run CUDA or PTX Code on a GPU Through MATLAB
7.3 Example: Vector Addition
7.4 Example: Matrix Multiplication
7.5 Chapter Review
Chapter 8: MATLAB MEX functions containing CUDA code
Abstract
8.1 A Brief Introduction to MATLAB MEX Files
8.2 Steps to Run MATLAB MEX Functions on GPU
8.3 Example: Vector Addition
8.4 Example: Matrix Multiplication
8.5 Chapter Review
Chapter 9: CUDA-accelerated libraries
Abstract
9.1 Introduction
9.2 cuBLAS
9.3 cuFFT
9.4 cuRAND
9.5 cuSOLVER
9.6 cuSPARSE
9.7 NPP
9.8 Thrust
9.9 Chapter Review
Chapter 10: Profiling code and improving GPU performance
Abstract
10.1 MATLAB Profiling
10.2 CUDA Profiling
10.3 Best Practices for Improving GPU Performance
10.4 Chapter Review
References
List of Examples
Index