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 knowledgeExplains the related
background on hardware, architecture and programming for ease of
useProvides simple worked examples of MATLAB and CUDA C codes as
well as templates that can be reused in real-world projects"
General
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