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gpu-parallelization

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Ferdib-Al-Islam / gpu_parallelization

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Co-occurrence matrices act as the input to many unsupervised learning algorithms, including those that learn word embedding, and modern spectral topic models. However, the computation of these inputs often takes longer time than the inference. While much thought has been given to implementing fast learning algorithms. The co-occurrence matrix co…

cuda cuda-toolkit cuda-c gpu-parallelization word-to-word-co-occurrence-matrix
  • Updated Oct 11, 2019
  • Jupyter Notebook

Vivek-Tate / Performance-Analysis-of-Parallel-Computing-Algorithms-using-CUDA-and-OpenMP

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This is an academic experiment comparing CPU and GPU performance using CUDA and OpenMP. It involves implementing three algorithms: Standard Deviation Calculation, Image Convolution, and Histogram-Based Data Structure, optimised for parallel execution to demonstrate performance improvements on different hardware architectures.

openmp parallel-computing cuda data-structures performance-analysis experiments cpu-monitoring image-convolution standard-deviation gpu-monitoring gpu-parallelization
  • Updated Jun 29, 2024
  • Cuda

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