GPU parallel computingLarge-scale dataANT COLONY OPTIMIZATIONDECISION-TREEALGORITHMSCLASSIFICATIONEvolutionary algorithms (EAs) are naturally prone to parallel processing. However, when they are applied to data mining, the fitness calculations start to dominate and the typical population-based decomposition ...
GPUGPGPUParallel computingThis paper mainly discusses the design, implementation and performance test of GPU-based parallel processing technology in deep packet inspection (DPI) field, which is used for building high-speed netdoi:10.1007/978-3-319-28121-6_5Zhimin Zhong...
Until 2006, using graphic chips for parallel computing was very difficult and required special techniques called general purpose computing on graphics processing units (GPGPU). With the advent of CUDA in 2007 by NVIDIA [9] GPU programming becomes more popular. Today, many programs in different ...
throwing a potential bottleneck into the timeliness of the design process. For many years, Speos has supported high-performance computing (HPC) on external clusters as an enhanced source of processing power for ray tracing, enabling supercomputer capabilities for running Speo...
ADLINK helps system developers, OEMs, and system integrators address these system requirements by leveraging embedded MXM GPU modules and PCI Express graphics cards. ADLINK’s GPU solutions based on NVIDIA® Quadro® embedded GPUs can accelerate the processing and rendering of images, video, and ...
DHS computes each neuron with multiple threads, which consumes a vast amount of threads when running neural network simulations. Graphics Processing Units (GPUs) consist of massive processing units (i.e., streaming processors, SPs, Fig.3a, b) for parallel computing46. In theory, many SPs on ...
A GPU-accelerated video processing application developed for CMSC416: Parallel Computing, leveraging CUDA and OpenCV to apply convolution-based effects like blurring, edge detection, and sharpening on video frames, with optimized performance using batch
DHS computes each neuron with multiple threads, which consumes a vast amount of threads when running neural network simulations. Graphics Processing Units (GPUs) consist of massive processing units (i.e., streaming processors, SPs, Fig.3a, b) for parallel computing46. In theory, many SPs on ...
In recent years, the use of graphics processing units (GPUs) to speed up scientific computations has become commonplace. This has been, in part, dictated by the slow-down in Moore’s law, and the progress made in the GPU architecture development, which has resulted in more computing capacity...
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