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Big Data Analytics on Modern Hardware Architectures: A Technology Survey

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Business Intelligence (eBISS 2012)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 138))

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Abstract

Big Data Analytics has the goal to analyze massive datasets, which increasingly occur in web-scale business intelligence problems. The common strategy to handle these workloads is to distribute the processing utilizing massive parallel analysis systems or to use big machines able to handle the workload. We discuss massively parallel analysis systems and their programming models. Furthermore, we discuss the application of modern hardware architectures for database processing. Today, many different hardware architectures apart from traditional CPUs can be used to process data. GPUs or FPGAs, among other new hardware, are usually employed as co-processors to accelerate query execution. The common point of these architectures is their massive inherent parallelism as well as a different programming model compared to the classical von Neumann CPUs. Such hardware architectures offer the processing capability to distribute the workload among the CPU and other processors, and enable systems to process bigger workloads.

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  1. Technische Universität Berlin, Berlin, Germany

    Michael Saecker & Volker Markl

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  1. Michael Saecker
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  1. MAS Laboratory, Ecole Centrale Paris, Châtenay-Malabry, France

    Marie-Aude Aufaure

  2. Department of Computer and Decision Engineering (CoDE), Université Libre de Bruxelles, Brussels, Belgium

    Esteban Zimányi

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Saecker, M., Markl, V. (2013). Big Data Analytics on Modern Hardware Architectures: A Technology Survey. In: Aufaure, MA., Zimányi, E. (eds) Business Intelligence. eBISS 2012. Lecture Notes in Business Information Processing, vol 138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36318-4_6

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