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Accelerating MATLAB with GPU Computing
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Accelerating MATLAB with
GPU Computing
A Primer with Examples
Jung W. Suh
Youngmin Kim
AMSTERDAM BOSTON HEIDELBERG LONDON
NEW YORK OXFORD PARIS SAN DIEGO
SAN FRANCISCO SINGAPORE SYDNEY TOKYO
Morgan Kaufmann is an imprint of Elsevier
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Acquiring Editor: Todd Green
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Morgan Kaufmann is an imprint of Elsevier
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First edition 2014
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Because of rapid advances in the medical sciences, in particular, independentverification
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1415161718 10987654321
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Preface
MATLAB is a widely used simulation tool for rapid prototyping and algorithm
development. Many laboratories and research institutions face growing demands to
run their MATLAB codes faster for computationally heavy projects after simple
simulations. Since MATLAB uses a vector/matrix representation of data, which is
suitable for parallel processing, it can benefit a lot from GPU acceleration.
Target Readers and Contents
This book is aimed primarily at the graduate students and researchers in the field of
engineering, science, and technology who need huge data processing without losing
the many benefits of MATLAB. However, MATLAB users come from various back-
grounds and do not necessarily have much programming experience. For those whose
backgrounds are not from programming, GPU acceleration for MATLAB may distract
their algorithm development and introduce unnecessary hassles, even when setting the
environment. This book targets the readers who have some or a lot of experience on
MATLAB coding but not enough depth in either C coding or the computer architec-
ture for parallelization. So readers can focus more on their research and work by
avoiding non-algorithmic hassles in using GPU and CUDA in MATLAB.
As a primer, the book will start with the basics, walking through the process of
setting MATLAB for CUDA (in Windows and Mac OSX), creating c-mex and m -file
profiling, then guide the users through the expert-level topics such as third-party
CUDA libraries. It also provides many practical ways to modify users’ MATLAB
codes to better utilize the immense computational power of graphics processors.
This book guides the reader to dramatically maximize the MATLAB speed
using NVIDIA’s Graphics Processing Unit (GPU). NVIDIA’s Compute Unified
Device Architecture (CUDA) is a parallel computing architecture originally
designed for computer games but is getting a reputation in the general science and
technology fields for its efficient massive computation power. From this book, the
reader can take advantage of the parallel processing power of GPU and abundant
CUDA scientific libraries for accelerating MATLAB code with no or less effort
and time, and bring readers’ researches and works to a higher level.
Directions of this Book
GPU Utilization Using c-mex Versus Parallel Computing Toolbox
This book deals with Mathworks’s Parallel Computing Toolbox in Chapter 5.
Although Mathworks’s Parallel Computing Toolbox is a useful tool for speeding
x
Preface
up MATLAB, the current version still has its limitation in making the Parallel
Computing Toolbox a general speeding-up solution, in addition to the extra cost of
purchasing the toolbox. Especially, since the Parallel Computing Toolbox targets
distributed computing over multicore, multiple computers and/or cluster machines
as well as GPU processing, GPU optimization for speeding up the user’s code is
comparatively limited both in speeding-up and supporting MATLAB functions.
Furthermore, if we limit to Mathworks’s the Parallel Computing Toolbox only,
then it is difficult to find an efficient way to utilize the abundant CUDA libraries to
their maximum. In this book, we address both the strengths and the limitations of
the current Parallel Computing Toolbox in Chapter 5. For the purpose of general
speeding up, GPU-utilization through c-mex proves a better approach and provides
more flexibility in current situation.
Tutorial Approach Versus Case Study Approach
As the book’s title says, we take more of a tutorial approach. MATLAB users may
come from many different backgrounds, and web resources are scattered over
Mathworks, NVIDIA, and private blogs as fragmented information. The tutorial
approach from setting the GPU environment to acquiring critical (but compressed)
hardware knowledge for GPU would be beneficial to prospective readers over a
wide spectrum. However, this book also has two chapters (Chapters 7 and 8) that
include case examples with working codes.
CUDA Versus OpenCL
When we prepared the proposal of this book, we also considered OpenCL as a
topic, because the inclusion of OpenCL would attract a wider range of readers.
However, while CUDA is more consistent and stable, because it is solely driven by
NVIDIA, the current OpenCL has no unified development environment and is still
unstable in some areas, because OpenCL is not governed by one company or insti-
tution. For this reason, installing, profiling, and debugging OpenCL are not yet
standardized. As a primer, this may distract the focus of this book. More impor-
tantly, for some reason Mathworks is very conservative in its support of OpenCL,
unlike CUDA. Therefore, we decided not to include OpenCL in this edition of our
book. However, we will again consider whether to include OpenCL in future edi-
tions if increased needs come from market or Mathworks’ direction changes.
After reading this book, the reader, in no time, will experience an amazing per-
formance boost in utilizing reader’s MATLAB codes and be better equipped in
research to enjoy the useful open-source resources for CUDA. The features this
book covers are available on Windows and Mac.
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