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Big Data Analytics on Modern Hardware Architectures: A Technology Survey.
Citation key DBLP:conf/ebiss/SaeckerM12
Author Michael Saecker, Volker Markl
Year 2012
Journal eBISS, European Business Intelligence Summer School, Part of the Lecture Notes in Business Information Processing book series (LNBIP)
Volume 138
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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