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General-purpose graphics processing units (GPGPU) have emerged as
an important class of shared memory parallel processing
architectures, with widespread deployment in every computer class
from high-end supercomputers to embedded mobile platforms. Relative
to more traditional multicore systems of today, GPGPUs have
distinctly higher degrees of hardware multithreading (hundreds of
hardware thread contexts vs. tens), a return to wide vector units
(several tens vs. 1-10), memory architectures that deliver higher
peak memory bandwidth (hundreds of gigabytes per second vs. tens),
and smaller caches/scratchpad memories (less than 1 megabyte vs.
1-10 megabytes). In this book, we provide a high-level overview of
current GPGPU architectures and programming models. We review the
principles that are used in previous shared memory parallel
platforms, focusing on recent results in both the theory and
practice of parallel algorithms, and suggest a connection to GPGPU
platforms. We aim to provide hints to architects about
understanding algorithm aspect to GPGPU. We also provide detailed
performance analysis and guide optimizations from high-level
algorithms to low-level instruction level optimizations. As a case
study, we use n-body particle simulations known as the fast
multipole method (FMM) as an example. We also briefly survey the
state-of-the-art in GPU performance analysis tools and techniques.
Table of Contents: GPU Design, Programming, and Trends /
Performance Principles / From Principles to Practice: Analysis and
Tuning / Using Detailed Performance Analysis to Guide Optimization
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Trilingual Renshi (Paperback)
Yasuhiro Yotsumoto Ming Di Don Mee Choi, Shuntaro Tanikawa, Hyesoon Kim
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R523
Discovery Miles 5 230
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Ships in 10 - 15 working days
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