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https://link.springer.com/article/10.1007%2Fs11227-008-0252-7
In recent years, the GPU (graphics processing unit) has evolved into an extremely powerful and flexible processor, with it now representing an attractive platform for general-purpose computation. Moreover, changes to the design and programmability of GPUs provide the opportunity to perform general-purpose computation on a GPU (GPGPU).Cited by: 4
https://www.researchgate.net/publication/220358398_Compiler_support_for_general-purpose_computation_on_GPUs
Request PDF Compiler support for general-purpose computation on GPUs In recent years, the GPU (graphics processing unit) has evolved into an extremely powerful and flexible processor, with it ...
http://developer.download.nvidia.com/assets/gamedev/docs/EG_04_GPGPU.pdf
The performance and flexibility of GPUs makes them an attractive platform for general-purpose computation Example applications (from www.GPGPU.org) Advanced Rendering: Global Illumination, Image-based Modeling Computational Geometry Computer Vision Image And Volume Processing
https://developer.nvidia.com/gpugems/gpugems2/part-iv-general-purpose-computation-gpus-primer/chapter-30-geforce-6-series-gpu
This avoids all fragment processor work on scissored (rejected) pixels. Scissoring is rarely useful for general-purpose computation because general-purpose programmers typically draw rectangles to perform computations in the first place. Next, the fragment's depth is …
https://developer.nvidia.com/gpugems/gpugems2/part-iv-general-purpose-computation-gpus-primer/chapter-33-implementing-efficient
Recent work in the emerging field of general-purpose computation on graphics processing units (GPGPU) has shown that GPUs are capable of accelerating applications as varied as fluid dynamics, advanced image processing, photorealistic rendering, and even computational chemistry and biology (Buck et al. 2004, Harris et al. 2004).
https://researcher.watson.ibm.com/researcher/files/us-bacon/Dubach12Compiling.pdf
Compiling a High-Level Language for GPUs (via Language Support for Architectures and Compilers) Christophe Dubach1, 2Perry Cheng Rodric Rabbah2 David F. Bacon Stephen J. Fink 1University of Edinburgh 2IBM Research [email protected] {perry,rabbah,dfb,sjfink}@us.ibm.com Abstract
https://software.intel.com/en-us/articles/pldi-tutorial-using-the-intelr-c-compiler-for-general-purpose-computation-offload-to-intelr
PLDI Tutorial: Using the Intel(R) C++ Compiler for General Purpose Computation Offload to Intel(R) Processor Graphics. Translating... This is a computer translation of the original content. It is provided for general information only and should not be relied upon as complete or accurate.Author: Anoop M.
http://http.download.nvidia.com/developer/presentations/2005/GDC/OpenGL_Day/OpenGL_GPGPU.pdf
GPU Gems 2. Programming Techniques for High-Performance Graphics and General-Purpose Computation. • 880 full-color pages, 330 figures, hard cover. • $59.99. • Experts from universities and industry. “The topics covered in GPU Gems 2 are critical to the next generation of game engines.”.
https://www.quora.com/Does-adding-a-graphic-card-boost-the-C-C++-compile-or-build-speed
Something compilers don't need or use much (except when optimizing floating point arithmetic in the program) GPUs are good at SIMD. i.e. performing the same operation on multiple inputs. This is again, not something a compiler needs to do. There may be a benefit if the compiler needs to, say, optimize several hundred floating point operations a...
https://en.wikipedia.org/wiki/Gpgpu
As of 2016, OpenCL is the dominant open general-purpose GPU computing language, and is an open standard defined by the Khronos Group. OpenCL provides a cross-platform GPGPU platform that additionally supports data parallel compute on CPUs. OpenCL is actively supported on Intel, AMD, Nvidia, and ARM platforms.
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