GPU Computing Gems, Emerald Edition (Applications of GPU by Wen-mei W. Hwu

By Wen-mei W. Hwu

"...the excellent significant other to Programming vastly Parallel Processors through Hwu & Kirk." -Nicolas Pinto, study Scientist at Harvard & MIT, NVIDIA Fellow 2009-2010

Graphics processing devices (GPUs) can do even more than render images. Scientists and researchers more and more glance to GPUs to enhance the potency and function of computationally-intensive experiments throughout more than a few disciplines.

GPU Computing gemstones: Emerald Edition brings their concepts to you, showcasing GPU-based ideas including:
* Black gap simulations with CUDA
* GPU-accelerated computation and interactive show of molecular orbitals
* Temporal facts mining for neuroscience
* GPU -based parallelization for quick circuit optimization
* quick graph cuts for computing device vision
* Real-time stereo on GPGPU utilizing innovative multi-resolution adaptive windows
* GPU photo demosaicing
* Tomographic picture reconstruction from unordered strains with CUDA
* scientific picture processing utilizing GPU -accelerated ITK photograph filters
* 41 extra chapters of leading edge GPU computing principles, written to be obtainable to researchers from any domain

GPU Computing gemstones: Emerald Edition is the 1st quantity in Morgan Kaufmann's Applications of GPU Computing Series, delivering the newest insights and examine in laptop imaginative and prescient, digital layout automation, rising data-intensive purposes, lifestyles sciences, scientific imaging, ray tracing and rendering, clinical simulation, sign and audio processing, statistical modeling, and video / photograph processing.

* Covers the breadth of from medical simulation and digital layout automation to audio / video processing, clinical imaging, machine imaginative and prescient, and more

* Many examples leverage NVIDIA's CUDA parallel computing structure, the main widely-adopted hugely parallel programming solution

* bargains insights and concepts in addition to useful "hands-on" abilities you could instantly positioned to use

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Additional info for GPU Computing Gems, Emerald Edition (Applications of GPU Computing Series)

Sample text

4) n=1 l=0 m=0 where we have replaced cνκ by cνnlm , with the vectors Rn = r − rn connecting the position rn of the nucleus of atom n to the desired spatial coordinate r. We have dropped the subscript p from the set of contraction coefficients {c} and exponents {ζ } with the understanding that each CGTO requires an additional summation over the primitives, as expressed in Eq. 3. The normalization factor Nζ ijk in Eq. 2 can be factored into a first part ηζ l that depends on both the exponent ζ and shell type l = i + j + k and a second part ηijk (= ηlm in terms of our alternative indexing) that depends only on the angular momentum, Nζ ijk = 3 4 2ζ π (8ζ )l · i!

This is typical for GPU algorithms, especially those relying on task parallelism.

The cost of evaluating ex by calling the CUDA routines expf() or expf(), or evaluating 2x via exp2f(), is much lower than on the CPU, yielding a performance benefit well beyond what would be expected purely as a result of effectively using the massively parallel GPU hardware. 3 Algorithms, Implementations, and Evaluations … GPU 2 GPU 1 GPU 0 MO 3-D lattice decomposes into 2-D slices (CUDA grids) Small 8 × 8 thread blocks afford large per-thread register count, shared memory Each thread computes one MO lattice point.

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