Showing 4 open source projects for "cuda gpu memtest windows"

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  • 1
    PyTorch Geometric

    PyTorch Geometric

    Geometric deep learning extension library for PyTorch

    It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of...
    Downloads: 0 This Week
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  • 2
    Bandicoot

    Bandicoot

    fast C++ library for GPU linear algebra & scientific computing

    * Fast GPU linear algebra library (matrix maths) for the C++ language, aiming towards a good balance between speed and ease of use * Provides high-level syntax and functionality deliberately similar to Matlab * Provides an API that is aiming to be compatible with Armadillo for easy transition between CPU and GPU linear algebra code * Useful for algorithm development directly in C++, or quick conversion of research code into production environments * Distributed under the permissive...
    Downloads: 5 This Week
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  • 3
    MXLib is a C++ wrapper around the Intel® Integrated Performance Primitives (IPP) library and NVidia NPP CUDA library. You can use either IPP code (or a subset of functions that do not require IPP) on the CPU side, or use NPP/CUDA on the GPU side, or use both together. The function syntax is similar to that found in MatLab and the library is designed to make it easy to port your code from MatLab to C++. The idea is to provide Scientists, Engineers, Researchers and other non full-time...
    Downloads: 0 This Week
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  • 4

    FreDec

    Parallelized FREquency DEComposer algorithm

    This is a parallelized algorithm performing a decomposition of the time-series data into a number of sinusoidal components, disentangling them from the white Gaussian noise. The algorithm analyses all suspicious frequencies, including the ones that look like an alias at a glance, but may become preferable later. After selection of the initial frequency candidates, the algorithm passes through all their possible combinations and estimates their multi-frequency statistical significance. In the...
    Downloads: 0 This Week
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