Showing 4 open source projects for "gpu-z"

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    PMCGPU

    PMCGPU

    Parallel simulators for Membrane Computing on the GPU

    Membrane Computing is a new research area (within Natural Computing) that aims to provide computing devices abstracted from the functioning and structure of living cells. These devices are called P systems. 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...
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  • 2
    FastFlow: programming multi-core

    FastFlow: programming multi-core

    Pattern-based multi/many-core parallel programming framework

    FastFlow is a C/C++ programming framework supporting the development of pattern-based parallel programs on multi/many-core, GPUs and distributed platforms. FastFlow run-time is built upon non-blocking threads and lock-free queues. Thanks to its very efficient CAS-free communication/synchronization support (e.g. few clock cycles core-to-core latency), FastFlow effectively supports the exploitation of fine grain parallelism, e.g. parallel codes managing very high frequency streams on...
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    Downloads: 2 This Week
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  • 3

    PrimeCoin CPU & GPU Miners

    Application for PrimeCoin [XPM] mining

    This is an application for PrimeCoin [XPM] cryptocurrency mining. You can mine PrimeCoins with just CPU or your video card (GPU)[GPU mining isn't available at this moment]. Current pre-alpha for x86 and x64 CPUs is based on original jhPrimeminer by jh00, but uses my own HugeInteger library instead of original GMP lib.
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  • 4

    LBP in multiple platforms

    LBP implementation in multiple computing platforms (ARM,GPU, DSP...)

    ...: - OpenCL for CPU & GPU - OpenCL for GPU (branchless) - C code optimized for ARM - OpenGL ES 2.0 shaders mobile GPUs - C code for TI C64x DSP core (branchless) - C code for TTA processor synthesis If you use the code somewhere, please cite: Bordallo López M., Nieto A., Boutellier J., Hannuksela J., and Silvén O. "Evaluation of real-time LBP computing in multiple architectures," Journal of Real Time Image Processing, 2014
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