Showing 4 open source projects for "cuda gpu"

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    Monte Carlo eXtreme (MCX)

    Monte Carlo eXtreme (MCX)

    Physically accurate and validated GPU ray-tracer

    MCX is a GPU-accelerated, general-purpose, physically-accurate and feature-rich 3-D light transport simulator. It is one of the fastest simulators because it can use tens of thousands of GPU threads to simulate photons in parallel.
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    Downloads: 71 This Week
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  • 2
    PMCGPU

    PMCGPU

    Parallel simulators for Membrane Computing on the GPU

    ...The objective of this project (PMCGPU) is to bring together all the researchers working on the development of parallel simulators for P systems, specially those using the GPU (e.g. CUDA, OpenCL, etc). Other parallel platforms are also welcome (multicore and manycore, FPGAs, etc). This project has been initiated by the Research Group on Natural Computing (Department of Computer Science and Artificial Intelligence, University of Seville). PMCGPU was born inside the P-Lingua project, of the same research group. ...
    Downloads: 0 This Week
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  • 3
    birgHPCC

    birgHPCC

    Rapid CUDA Cluster Deployment

    ...While retaining the major functions of the original birgHPC, including automated computing cluster conversion and auto slots detection, the new version (birgHPCC) is capable of creating and configuring a compute unified device architecture (CUDA) computing cluster, hence the extra “C” in the name. In addition to the increase in image size (less than 2 gigabytes) and a new Linux base (previously Debian, now Ubuntu), CUDA-capable bioinformatics software programs, such as NAMD, HOOMD-blue, VMD, GPU-HMMER and GPU-BLAST, are pre-installed in birgHPCC, along with the CUDA driver, libraries and software development kit (SDK). ...
    Downloads: 0 This Week
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  • 4

    Rozkład temperatury w pomieszczeniach

    Symulacja procesów cieplnych zachodzących w pomieszczeniach

    ...Piotr Fiertek Celem projektu jest stworzenie środowiska symulacyjnego dla procesów termicznych zachodzących w pomieszczeniach. Integralną jego częścią jest implementacja algorytmów obliczeniowych na procesorach graficznych GPU przy użyciu standardów CUDA oraz OpenCL. Innymi ważnymi elementami są graficzna wizualizacja wyników z wykorzystaniem biblioteki graficznej OpenGL oraz stworzenie interfejsu użytkownika umożliwiającego zmianę parametrów symulacji.
    Downloads: 0 This Week
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