Abstract
Performance and efficiency became recently key requirements of computer architectures. Modern computers incorporate Graphics Processing Units (GPUs) into running data mining algorithms, as well as other general purpose computations. In this paper, different parallelization methods are analyzed and compared in order to understand their applicability. From multi-threading on shared memory to using NVIDIA’s GPU accelerators for increasing performance and efficiency on parallel computing, this work discusses the parallelization of data mining algorithms considering performance and efficiency issues. The performance is compared on both many-core systems and GPU accelerators on a distance measure algorithm using a relatively big data set. We optimize the way we deal with GPUs in heterogeneous systems to make them more suitable for big data mining applications with heavy distance calculations. Moreover, we focus on achieving a higher utilization of GPU resources and a better reuse of data. Our implementation of the content-based similarity algorithm SQFD on the GPU outperforms by up to 50× CPU counterparts, and up to 15× CPU multi-threaded implementations.
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Tarakji, A., Hassani, M., Lankes, S., Seidl, T. (2013). Using a Multitasking GPU Environment for Content-Based Similarity Measures of Big Data. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39640-3_13
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DOI: https://doi.org/10.1007/978-3-642-39640-3_13
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