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Emergent models, frameworks, and hardware technologies for Big data analytics

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Abstract

Today’s state-of-the-art Big data analytics engines handle masses of data, but will reach to their limits, as the future Big data flood is predicted to still grow with an increasing speed. Hence we need to think about the next development phase and future features of Big data analytics engines. In this paper, we discuss possible future enhancements in the area of Big data analytics with focus on emergent models, frameworks, and hardware technologies. We point out a selection of new challenges and open research questions.

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Notes

  1. As given in the November list at https://www.top500.org.

  2. Floating-point operations per second (Flops) is a measure of computer performance representing the number of floating-point calculations computed per second.

  3. https://software.intel.com/en-us/hardware-accelerator-research-program.

  4. Inspired by [77], the numbers for the considered hardware are collected from https://www.intel.com/content/www/us/en/support/articles/000006779/processors.html for Intel Xeon processors, http://www.intel.com/content/dam/www/public/us/en/documents/product-briefs/high-performance-xeon-phi-coprocessor-brief.pdf and https://www.intel.com/content/www/us/en/processors/xeon/scalable/xeon-scalable-platform-brief.html for Xeon Phi, http://en.wikipedia.org/wiki/List_of_Nvidia_graphics_processing_units for NVIDIA GPUs and http://en.wikipedia.org/wiki/Comparison_of_AMD_graphics_processing_units for AMD GPUs.

  5. We define the number of computational units of CPUs as the number of cores, and compare these number with the number of streaming multi-processors (NVIDIA GPU) and compute units (AMD GPU). We do not present here the number of compute units of GPUs, groups of which are meant for handling single-instruction multiple-data (SIMD) in GPUs and hence are not independent of each other as the cores of CPUs.

  6. Note that there is a big discussion of about how to determine the GFlops of an FPGA (see, e.g., https://www.altera.com/en_US/pdfs/literature/wp/wp-01222-understanding-peak-floating-point-performance-claims.pdf). We considered the numbers presented in previously mentioned web document. Further note that due to the different architectures it is much more difficult to define the number of computational units of an FPGA, which are comparable to those of CPUs and GPUs. Hence we do not provide the numbers of computational units for FPGAs here.

  7. http://spark.apache.org/.

  8. http://storm.apache.org/.

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Groppe, S. Emergent models, frameworks, and hardware technologies for Big data analytics. J Supercomput 76, 1800–1827 (2020). https://doi.org/10.1007/s11227-018-2277-x

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