4 projects for "intel gpu" with 1 filter applied:

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  • 1
    waifu2x ncnn Vulkan

    waifu2x ncnn Vulkan

    waifu2x converter ncnn version, run fast GPU with vulkan

    ncnn implementation of waifu2x converter. Runs fast on Intel/AMD/Nvidia/Apple-Silicon with Vulkan API. waifu2x-ncnn-vulkan uses ncnn project as the universal neural network inference framework.
    Downloads: 11 This Week
    Last Update:
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  • 2

    SAGECAL

    GPU/MIC accelerated radio interferometric calibration program

    SAGECal is a very fast, memory efficient and GPU accelerated radio interferometric calibration program. It can handle all source models including points, Gaussians and Shapelets. It can calibrate along hundreds of directions without running out of memory in almost real time. Intel Xeon Phi acceleration is also available. Distributed calibration using MPI and consensus optimization is enabled.
    Downloads: 2 This Week
    Last Update:
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  • 3

    Accelerated Feature Extraction Tool

    A fast GPU accelerated feature extraction software for speech analysis

    A fast feature extraction software tool for speech analysis and processing. It incorporates standard MFCC, PLP, and TRAPS features. The tool is a specially designed to process very large audio data sets. It uses GPU acceleration if compatible GPU available (CUDA as weel as OpenCL, NVIDIA, AMD, and Intel GPUs are supported). CPU SSE intrinsic instruction set is used in cases where no compatible GPU present. The output files are stored in HTK format. The software is developed at Department of Cybernetics at University of West Bohemia in Pilsen.
    Downloads: 0 This Week
    Last Update:
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  • 4
    MICA-aligner

    MICA-aligner

    Next-generation sequencing short reads aligner based on Intel® MIC

    Latest Code in GitHub: https://github.com/aquaskyline/MICA-aligner To better utilize MIC-enabled computers for NGS data analysis, we developed a new short-read aligner MICA that is optimized in view of MIC’s limitation and the extra parallelism inside each MIC core. Experiments on aligning 150bp paired-end reads show that MICA using one MIC board is ~4.85 times faster than the CPU-(multi-core)-based BWA-MEM and about the same speed as the GPU-based SOAP3-dp. Furthermore, MICA’s...
    Downloads: 0 This Week
    Last Update:
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