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Through the algorithmic design patterns of data parallelism and task parallelism, the Graphics Processing Unit (GPU) offers the potential to vastly accelerate discovery and innovation across a multitude of disciplines. For example, the exponential growth in data volume now presents an obstacle for high-throughput data mining in fields such as neuroscience and bioinformatics. As such, one presents a characterization of a MapReduce-based data-mining application on a General-Purpose GPU (GPGPU). Using neuroscience as the application vehicle, the results of the multidimensional performance evaluation show that a "One-size-fits-all" approach maps poorly across different GPGPU cards.
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