Download GPU Computing Gems, Emerald Edition (Applications of GPU by Wen-mei W. Hwu PDF
By Wen-mei W. Hwu
"...the ideal significant other to Programming hugely Parallel Processors through Hwu & Kirk." -Nicolas Pinto, examine Scientist at Harvard & MIT, NVIDIA Fellow 2009-2010
Graphics processing devices (GPUs) can do even more than render pics. Scientists and researchers more and more glance to GPUs to enhance the potency and function of computationally-intensive experiments throughout a variety of disciplines.
GPU Computing gemstones: Emerald Edition brings their suggestions to you, showcasing GPU-based recommendations including:
* Black gap simulations with CUDA
* GPU-accelerated computation and interactive show of molecular orbitals
* Temporal info mining for neuroscience
* GPU -based parallelization for speedy circuit optimization
* quickly graph cuts for desktop vision
* Real-time stereo on GPGPU utilizing revolutionary multi-resolution adaptive windows
* GPU photograph demosaicing
* Tomographic picture reconstruction from unordered strains with CUDA
* clinical snapshot processing utilizing GPU -accelerated ITK photo filters
* 41 extra chapters of cutting edge GPU computing rules, written to be available to researchers from any domain
GPU Computing gemstones: Emerald Edition is the 1st quantity in Morgan Kaufmann's Applications of GPU Computing Series, providing the most recent insights and examine in laptop imaginative and prescient, digital layout automation, rising data-intensive functions, lifestyles sciences, clinical imaging, ray tracing and rendering, medical simulation, sign and audio processing, statistical modeling, and video / photo processing.
* Covers the breadth of from clinical simulation and digital layout automation to audio / video processing, scientific imaging, desktop imaginative and prescient, and more
* Many examples leverage NVIDIA's CUDA parallel computing structure, the main widely-adopted vastly parallel programming solution
* deals insights and ideas in addition to functional "hands-on" talents you could instantly positioned to use
Read Online or Download GPU Computing Gems, Emerald Edition (Applications of GPU Computing Series) PDF
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The abstracts and papers during this quantity have been offered on the 5th Annual foreign Computing and Combinatorics convention (COCOON ’99), which used to be held in Tokyo, Japan from July 26 to twenty-eight, 1999. the subjects disguise such a lot features of theoretical desktop technological know-how and combinatorics referring to computing.
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Additional info for GPU Computing Gems, Emerald Edition (Applications of GPU Computing Series)
Example text
The SSE expf() routine was hand-coded using intrinsics that are compiled directly into x86 SSE machine instructions. The resulting “icc-sse-cephes” kernel has previously been shown to outperform the CPU algorithms implemented in the popular MacMolPlt and Molekel visualization tools, and it can be taken to be a representative peak-performance CPU reference [1]. The single-core CPU result for the “icc-sse-cephes” kernel was selected as the basis for normalizing performance results because it represents the best-case single-core CPU performance.
The latter method uses the principle of inclusion-exclusion between sets to compute the same union of all atoms. 1 is motivated by considering each atom to be a set of points, and constructing the union of these sets. Defining atomic densities as indicator functions (one inside a given radius around a point and zero outside) generates the “hard-sphere” model of molecular shape. This model has several shortcomings (including nondifferentiability) that makes it difficult to use in computations. 2) Such Gaussian functions are smooth and differentiable.
For a multi-GPU calculation, the set of slices is dynamically distributed among the pool of available GPUs. Each of the GPUs requests a slice index to compute, computes the assigned slice, and stores the result at the appropriate location in host memory. Each planar slice computed on a GPU is decomposed into a 2-D CUDA grid consisting of fixedsize 8 × 8 thread blocks. As the size of the 3-D lattice increases, the number of planar slices increases, and the number of thread blocks in each CUDA grid increases accordingly.