PACKT (406)
Text Book 교재용원서 (674)
컴퓨터공학 (797)
컴퓨터 일반도서 (551)
전기,전자공학 (699)
기계공학 (188)
재료공학 (31)
에너지공학 (65)
의용공학 (37)
생명과학 (224)
물리학 (424)
지구과학 (74)
천문학 (38)
수학 (102)
통계학 (45)
경영학 (40)
산업공학 (12)
사회복지학 (5)
심리학 (247)
교육학 (1)
화학 (4)
기타 (61)
특가할인도서 (86)

> > 컴퓨터공학 > 그래픽

이미지를 클릭하시면 큰 이미지를 보실 수 있습니다.
GPU Programming in MATLAB
출판사 : Morgan Kaufmann
저 자 : Ploskas
ISBN : 9780128051320
발행일 : 2016-7
도서종류 : 외국도서
발행언어 : 영어
페이지수 : 318
판매가격 : 55,000원
판매여부 : 재고확인요망
주문수량 : [+]수량을 1개 늘입니다 [-]수량을 1개 줄입니다

My Wish List 에 저장하기
   GPU Programming in MATLAB 목차

Table of Contents
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
   도서 상세설명   


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.

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

  교육용 보조자료   
작성된 교육용 보조자료가 없습니다.