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WIREs Data Mining Knowl Discov
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Accelerating data mining workloads: current approaches and future challenges in system architecture design

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Abstract Conventional systems based on general‐purpose processors cannot keep pace with the exponential increase in the generation and collection of data. It is therefore important to explore alternative architectures that can provide the computational capabilities required to analyze ever‐growing datasets. Programmable graphics processing units (GPUs) offer computational capabilities that surpass even high‐end multi‐core central processing units (CPUs), making them well‐suited for floating‐point‐ or integer‐intensive and data parallel operations. Field‐programmable gate arrays (FPGAs), which can be reconfigured to implement an arbitrary circuit, provide the capability to specify a customized datapath for any task. The multiple granularities of parallelism offered by FPGA architectures, as well as their high internal bandwidth, make them suitable for low complexity parallel computations. GPUs and FPGAs can serve as coprocessors for data mining applications, allowing the CPU to offload computationally intensive tasks for faster processing. Experiments have shown that heterogeneous architectures employing GPUs or FPGAs can result in significant application speedups over homogenous CPU‐based systems, while increasing performance per watt. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 41‐54 DOI: 10.1002/widm.9 This article is categorized under: Application Areas > Data Mining Software Tools Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Computer Architectures for Data Mining

Multiphased data burst and kernel operations seen in data mining.

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FPGA implementation of principal component analysis.

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Logic implementing Gini calculation.

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DTC implementation on FPGA.

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k‐means implementation on FPGA.

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Generic implementation of FPGA logic cells.

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Speedup of GPU versus CPU for different data mining algorithms.

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Speedup of GPU versus CPU for basic statistical kernels for different data sizes.

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Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining
Application Areas > Data Mining Software Tools
Technologies > Computer Architectures for Data Mining

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