Showing 79 open source projects for "gpu faster"

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    SOAP3-DP

    SOAP3-DP

    Fast, Accurate and Sensitive GPU-based Short Read Aligner

    Latest Code on GitHub: https://github.com/aquaskyline/SOAP3-dp SOAP3-dp, through leveraging the computational power of both CPU and GPU with optimized algorithms, delivers high speed and sensitivity simultaneously. Compared with widely adopted aligners including BWA, Bowtie2, SeqAlto, CUSHAW2, GEM and GPU-based aligners BarraCUDA and CUSHAW, SOAP3-dp was found to be two to tens of times faster, while maintaining the highest sensitivity and lowest false discovery rate (FDR) on Illumina reads with different lengths. ...
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  • 2
    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 simplicity allows very efficient scale-up when multiple MIC boards are used in a node (3 cards gives a 14-fold speedup over 6-core BWA-MEM).
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  • 3
    Simbuca
    SIMBUCA (before called Simonion) is a simulation package that simulates the motion of charged particles under the influence of Electric and/or Magnetic fields. What makes Simbuca unique is that you can choose to calculate the Coulomb interaction between ions on a Graphics cards which is much faster than calculating this on the conventional CPU (reducing the simulation time from years to days). Therefore Simbuca has been applied in various projects which required to understand the...
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  • 4

    GENIE (GEne-geNe IntEraction)

    GPU based Parallel Gene-Gene Interaction Analysis

    ...Most of the latest Central Processing Units (CPUs) have multiple cores, whereas Graphics Processing Units (GPUs) also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. ...
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