---
_id: '16411'
abstract:
- lang: eng
  text: FPGA devices have been proving to be good candidates to accelerate applications
    from different research topics. For instance, machine learning applications such
    as K-Means clustering usually relies on large amount of data to be processed,
    and, despite the performance offered by other architectures, FPGAs can offer better
    energy efficiency. With that in mind, Intel has launched a platform that integrates
    a multicore and an FPGA in the same package, enabling low latency and coherent
    fine-grained data offload. In this paper, we present a parallel implementation
    of the K-Means clustering algorithm, for this novel platform, using OpenCL language,
    and compared it against other platforms. We found that the CPU+FPGA platform was
    more energy efficient than the CPU-only approach from 70.71% to 85.92%, with Standard
    and Tiny input sizes respectively, and up to 68.21% of performance improvement
    was obtained with Tiny input size. Furthermore, it was up to 7.2×more energy efficient
    than an Intel® Xeon Phi ™, 21.5×than a cluster of Raspberry Pi boards, and 3.8×than
    the low-power MPPA-256 architecture, when the Standard input size was used.
author:
- first_name: Matheus A.
  full_name: Souza, Matheus A.
  last_name: Souza
- first_name: Lucas A.
  full_name: Maciel, Lucas A.
  last_name: Maciel
- first_name: Pedro Henrique
  full_name: Penna, Pedro Henrique
  last_name: Penna
- first_name: Henrique C.
  full_name: Freitas, Henrique C.
  last_name: Freitas
citation:
  ama: 'Souza MA, Maciel LA, Penna PH, Freitas HC. Energy Efficient Parallel K-Means
    Clustering for an Intel® Hybrid Multi-Chip Package. In: <i>2018 30th International
    Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)</i>.
    ; 2019. doi:<a href="https://doi.org/10.1109/cahpc.2018.8645850">10.1109/cahpc.2018.8645850</a>'
  apa: Souza, M. A., Maciel, L. A., Penna, P. H., &#38; Freitas, H. C. (2019). Energy
    Efficient Parallel K-Means Clustering for an Intel® Hybrid Multi-Chip Package.
    In <i>2018 30th International Symposium on Computer Architecture and High Performance
    Computing (SBAC-PAD)</i>. <a href="https://doi.org/10.1109/cahpc.2018.8645850">https://doi.org/10.1109/cahpc.2018.8645850</a>
  bibtex: '@inproceedings{Souza_Maciel_Penna_Freitas_2019, title={Energy Efficient
    Parallel K-Means Clustering for an Intel® Hybrid Multi-Chip Package}, DOI={<a
    href="https://doi.org/10.1109/cahpc.2018.8645850">10.1109/cahpc.2018.8645850</a>},
    booktitle={2018 30th International Symposium on Computer Architecture and High
    Performance Computing (SBAC-PAD)}, author={Souza, Matheus A. and Maciel, Lucas
    A. and Penna, Pedro Henrique and Freitas, Henrique C.}, year={2019} }'
  chicago: Souza, Matheus A., Lucas A. Maciel, Pedro Henrique Penna, and Henrique
    C. Freitas. “Energy Efficient Parallel K-Means Clustering for an Intel® Hybrid
    Multi-Chip Package.” In <i>2018 30th International Symposium on Computer Architecture
    and High Performance Computing (SBAC-PAD)</i>, 2019. <a href="https://doi.org/10.1109/cahpc.2018.8645850">https://doi.org/10.1109/cahpc.2018.8645850</a>.
  ieee: M. A. Souza, L. A. Maciel, P. H. Penna, and H. C. Freitas, “Energy Efficient
    Parallel K-Means Clustering for an Intel® Hybrid Multi-Chip Package,” in <i>2018
    30th International Symposium on Computer Architecture and High Performance Computing
    (SBAC-PAD)</i>, 2019.
  mla: Souza, Matheus A., et al. “Energy Efficient Parallel K-Means Clustering for
    an Intel® Hybrid Multi-Chip Package.” <i>2018 30th International Symposium on
    Computer Architecture and High Performance Computing (SBAC-PAD)</i>, 2019, doi:<a
    href="https://doi.org/10.1109/cahpc.2018.8645850">10.1109/cahpc.2018.8645850</a>.
  short: 'M.A. Souza, L.A. Maciel, P.H. Penna, H.C. Freitas, in: 2018 30th International
    Symposium on Computer Architecture and High Performance Computing (SBAC-PAD),
    2019.'
date_created: 2020-04-06T09:41:41Z
date_updated: 2022-01-06T06:52:50Z
doi: 10.1109/cahpc.2018.8645850
keyword:
- pc2-harp-ressources
language:
- iso: eng
publication: 2018 30th International Symposium on Computer Architecture and High Performance
  Computing (SBAC-PAD)
publication_identifier:
  isbn:
  - '9781538677698'
publication_status: published
status: public
title: Energy Efficient Parallel K-Means Clustering for an Intel® Hybrid Multi-Chip
  Package
type: conference
user_id: '61189'
year: '2019'
...