GPU Programming in MATLAB

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. 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.

9.8 Thrust.

Front Cover; GPU Programming in MATLAB; Copyright; Dedication; Contents; About the Authors; Foreword; Preface; Chapter 1: Introduction; 1.1 Parallel programming; 1.1.1 Introduction to Parallel Computing; 1.1.2 Classification of Parallel Computers; 1.1.3 Parallel Computers' Memory Architectures; 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; 2.1 Hardware Requirements; 2.2 Software requirements; 2.2.1 NVIDIA CUDA Toolkit; Windows; Linux.

MAC OS2.2.2 MATLAB; Windows; Linux; MAC OS; 2.3 Chapter review; Chapter 3: Parallel Computing Toolbox; 3.1 Product Description and Objectives; 3.2 Parallel for-loops (parfor); Example: Monte Carlo simulation to approximate the area of a figure; 3.3 Single program multiple data (spmd); Example: Monte Carlo simulation to approximate the area of a figure; 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.

4.1 GPU Programming Features in MA…