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 unif ied 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 debuggi ng 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 Ope nCL 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 amazi ng 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.
x Preface
