Accelerating MATLAB with GPU Computing

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap. Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers’ projects. Download example codes from the publisher's website: http://booksite.elsevier.com/9780124080805/ Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledge Explains the related background on hardware, architecture and programming for ease of use Provides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects

Produk Detail:

  • Author : Jung W. Suh
  • Publisher : Newnes
  • Pages : 258 pages
  • ISBN : 0124079164
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKAccelerating MATLAB with GPU Computing

Accelerating MATLAB with GPU Computing

Accelerating MATLAB with GPU Computing
  • Author : Jung W. Suh,Youngmin Kim
  • Publisher : Newnes
  • Release : 18 November 2013
GET THIS BOOKAccelerating MATLAB with GPU Computing

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap. Starting with the

GPU Programming in MATLAB

GPU Programming in MATLAB
  • Author : Nikolaos Ploskas,Nikolaos Samaras
  • Publisher : Morgan Kaufmann
  • Release : 25 August 2016
GET THIS BOOKGPU 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

Accelerating MATLAB Performance

Accelerating MATLAB Performance
  • Author : Yair M. Altman
  • Publisher : CRC Press
  • Release : 11 December 2014
GET THIS BOOKAccelerating MATLAB Performance

The MATLAB® programming environment is often perceived as a platform suitable for prototyping and modeling but not for "serious" applications. One of the main complaints is that MATLAB is just too slow. Accelerating MATLAB Performance aims to correct this perception by describing multiple ways to greatly improve MATLAB program speed. Packed with thousands of helpful tips, it leaves no stone unturned, discussing every aspect of MATLAB. Ideal for novices and professionals alike, the book describes MATLAB performance in a scale

GPU Computing Gems Emerald Edition

GPU Computing Gems Emerald Edition
  • Author : Anonim
  • Publisher : Elsevier
  • Release : 13 January 2011
GET THIS BOOKGPU Computing Gems Emerald Edition

GPU Computing Gems Emerald Edition offers practical techniques in parallel computing using graphics processing units (GPUs) to enhance scientific research. The first volume in Morgan Kaufmann's Applications of GPU Computing Series, this book offers the latest insights and research in computer vision, electronic design automation, and emerging data-intensive applications. It also covers life sciences, medical imaging, ray tracing and rendering, scientific simulation, signal and audio processing, statistical modeling, video and image processing. This book is intended to help those who

Recent Progress in Parallel and Distributed Computing

Recent Progress in Parallel and Distributed Computing
  • Author : Wen-Jyi Hwang
  • Publisher : BoD – Books on Demand
  • Release : 19 July 2017
GET THIS BOOKRecent Progress in Parallel and Distributed Computing

Parallel and distributed computing has been one of the most active areas of research in recent years. The techniques involved have found significant applications in areas as diverse as engineering, management, natural sciences, and social sciences. This book reports state-of-the-art topics and advances in this emerging field. Completely up-to-date, aspects it examines include the following: 1) Social networks; 2) Smart grids; 3) Graphic processing unit computation; 4) Distributed software development tools; 5) Analytic hierarchy process and the analytic network process

OpenCL Programming by Example

OpenCL Programming by Example
  • Author : Ravishekhar Banger,Koushik Bhattacharyya
  • Publisher : Packt Publishing Ltd
  • Release : 23 December 2013
GET THIS BOOKOpenCL Programming by Example

This book follows an example-driven, simplified, and practical approach to using OpenCL for general purpose GPU programming. If you are a beginner in parallel programming and would like to quickly accelerate your algorithms using OpenCL, this book is perfect for you! You will find the diverse topics and case studies in this book interesting and informative. You will only require a good knowledge of C programming for this book, and an understanding of parallel implementations will be useful, but not

CUDA Programming

CUDA Programming
  • Author : Shane Cook
  • Publisher : Newnes
  • Release : 11 April 2021
GET THIS BOOKCUDA Programming

If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to

Professional CUDA C Programming

Professional CUDA C Programming
  • Author : John Cheng,Max Grossman,Ty McKercher
  • Publisher : John Wiley & Sons
  • Release : 09 September 2014
GET THIS BOOKProfessional CUDA C Programming

Break into the powerful world of parallel GPU programming with this down-to-earth, practical guide Designed for professionals across multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease the development of GPU programming -- fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement parallel algorithms on GPUs. Each chapter covers a specific topic, and includes workable examples that demonstrate the development process, allowing readers

Accelerating MATLAB Performance

Accelerating MATLAB Performance
  • Author : Yair M. Altman
  • Publisher : CRC Press
  • Release : 11 December 2014
GET THIS BOOKAccelerating MATLAB Performance

The MATLAB® programming environment is often perceived as a platform suitable for prototyping and modeling but not for "serious" applications. One of the main complaints is that MATLAB is just too slow. Accelerating MATLAB Performance aims to correct this perception by describing multiple ways to greatly improve MATLAB program speed. Packed with thousands of helpful tips, it leaves no stone unturned, discussing every aspect of MATLAB. Ideal for novices and professionals alike, the book describes MATLAB performance in a scale

Neural Networks with MATLAB

Neural Networks with MATLAB
  • Author : Marvin L.
  • Publisher : Createspace Independent Publishing Platform
  • Release : 23 October 2016
GET THIS BOOKNeural Networks with MATLAB

Neural Network Toolbox provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more importan features are

CUDA Application Design and Development

CUDA Application Design and Development
  • Author : Rob Farber
  • Publisher : Elsevier
  • Release : 11 April 2021
GET THIS BOOKCUDA Application Design and Development

Machine generated contents note: 1. How to think in CUDA 2. Tools to build, debug and profile 3. The GPU performance envelope 4. The CUDA memory subsystems 5. Exploiting the CUDA execution grid 6. MultiGPU applications and scaling 7. Numerical CUDA, libraries and high-level language bindings 8. Mixing CUDA with rendering 9. High Performance Machine Learning 10. Scientific Visualization 11. Multimedia with OpenCV 12. Ultra Low-power Devices: Tegra.

Heterogeneous Computing with OpenCL 2.0

Heterogeneous Computing with OpenCL 2.0
  • Author : David R. Kaeli,Perhaad Mistry,Dana Schaa,Dong Ping Zhang
  • Publisher : Morgan Kaufmann
  • Release : 18 June 2015
GET THIS BOOKHeterogeneous Computing with OpenCL 2.0

Heterogeneous Computing with OpenCL 2.0 teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs). This fully-revised edition includes the latest enhancements in OpenCL 2.0 including: • Shared virtual memory to increase programming flexibility and reduce data transfers that consume resources • Dynamic parallelism which reduces processor load and avoids bottlenecks • Improved imaging support and integration with OpenGL Designed to work on multiple platforms, OpenCL will help you more

Recent Progress in Parallel and Distributed Computing

Recent Progress in Parallel and Distributed Computing
  • Author : Wen-Jyi Hwang
  • Publisher : BoD – Books on Demand
  • Release : 19 July 2017
GET THIS BOOKRecent Progress in Parallel and Distributed Computing

Parallel and distributed computing has been one of the most active areas of research in recent years. The techniques involved have found significant applications in areas as diverse as engineering, management, natural sciences, and social sciences. This book reports state-of-the-art topics and advances in this emerging field. Completely up-to-date, aspects it examines include the following: 1) Social networks; 2) Smart grids; 3) Graphic processing unit computation; 4) Distributed software development tools; 5) Analytic hierarchy process and the analytic network process