Frequent Items Mining Acceleration Exploiting Fast Parallel Sorting on the GPU | Zendy

Ugo Erra | Zendy; Bernardino Frola | Zendy

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Frequent Items Mining Acceleration Exploiting Fast Parallel Sorting on the GPU

Author(s) -

Ugo Erra,

Bernardino Frola

Publication year - 2012

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2012.04.010

Subject(s) - computer science , sorting , graphics , implementation , parallel computing , cuda , process (computing) , throughput , field programmable gate array , acceleration , general purpose computing on graphics processing units , skewness , field (mathematics) , exploit , embedded system , algorithm , computer graphics (images) , wireless , operating system , physics , classical mechanics , statistics , mathematics , computer security , pure mathematics , programming language

In this paper, we show how to employ Graphics Processing Units (GPUs) to provide an effcient and highperformance solution for finding frequent items in data streams. We discuss several design alternatives and present an implementation that exploits the great capability of graphics processors in parallel sorting. We provide an exhaustive evaluation of performances, quality results and several design trade-offs. Onanoff-the-shelf GPU, the fastest of our implementations can process over 200 million items per second, which is better than the best known solution based on Field Programmable Gate Arrays (FPGAs) and CPUs. Moreover, in previous approaches, performances are directly related to the skewness of the input data distribution, while in our approach, the high throughput is independent from this factor

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