Extending a Highly Parallel Data Mining Algorithm to the Intel ® Many Integrated Core Architecture

  • Alexander Heinecke
  • Michael Klemm
  • Dirk Pflüger
  • Arndt Bode
  • Hans-Joachim Bungartz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7156)


Extracting knowledge from vast datasets is a major challenge in data-driven applications, such as classification and regression, which are mostly compute bound. In this paper, we extend our SG + +  algorithm to the Intel® Many Integrated Core Architecture (Intel® MIC Architecture). The ease of porting an application to Intel MIC Architecture is shown: porting existing SSE code is very easy and straightforward. We evaluate the current prototype pre-release coprocessor board codenamed Intel® “Knights Ferry”. We utilize the pragma-based offloading programming model offered by the Intel® Composer XE for Intel MIC Architecture, generating both the host and the coprocessor code. We compare the achieved performance with an NVIDIA C2050 accelerator and show that the pre-release Knights Ferry coprocessor delivers better performance than the C2050 and exceeds the C2050 when comparing the productivity aspect of implementing algorithms for the coprocessors.


Intel® Many Integrated Core Architecture Intel® MIC Architecture Intel® Knights Ferry NVIDIA Fermi* GPGPU accelerators coprocessors data mining sparse grids 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alexander Heinecke
    • 1
  • Michael Klemm
    • 3
  • Dirk Pflüger
    • 1
  • Arndt Bode
    • 2
  • Hans-Joachim Bungartz
    • 2
  1. 1.Technische Universität MünchenGarchingGermany
  2. 2.Leibniz-Rechenzentrum der Bayerischen Akademie der WissenschaftenGarchingGermany
  3. 3.Intel GmbHFeldkirchenGermany

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