Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation

Porrmann F, Pilz S, Stella A, Kleinjohann A, Denker M, Hagemeyer J, Rückert U (2021)
Frontiers in Neuroinformatics 15(15): 723406.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Porrmann, FlorianUniBi; Pilz, SarahUniBi; Stella, Alessandra; Kleinjohann, Alexander; Denker, Michael; Hagemeyer, JensUniBi; Rückert, UlrichUniBi
Abstract / Bemerkung
The SPADE (spatio-temporal Spike PAttern Detection and Evaluation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85–90% of the method's total runtime. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE, which significantly accelerates pattern mining and result filtering. Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude.
Stichworte
FP-growth; pattern mining; spike train analysis; embedded devices; performance optimization; low power; parallel and distributed computing; heterogeneous computing
Erscheinungsjahr
2021
Zeitschriftentitel
Frontiers in Neuroinformatics
Band
15
Ausgabe
15
Art.-Nr.
723406
eISSN
1662-5196
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Universität Bielefeld gefördert.
Page URI
https://pub.uni-bielefeld.de/record/2957481

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Porrmann F, Pilz S, Stella A, et al. Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation. Frontiers in Neuroinformatics. 2021;15(15): 723406.
Porrmann, F., Pilz, S., Stella, A., Kleinjohann, A., Denker, M., Hagemeyer, J., & Rückert, U. (2021). Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation. Frontiers in Neuroinformatics, 15(15), 723406. https://doi.org/10.3389/fninf.2021.723406
Porrmann, Florian, Pilz, Sarah, Stella, Alessandra, Kleinjohann, Alexander, Denker, Michael, Hagemeyer, Jens, and Rückert, Ulrich. 2021. “Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation”. Frontiers in Neuroinformatics 15 (15): 723406.
Porrmann, F., Pilz, S., Stella, A., Kleinjohann, A., Denker, M., Hagemeyer, J., and Rückert, U. (2021). Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation. Frontiers in Neuroinformatics 15:723406.
Porrmann, F., et al., 2021. Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation. Frontiers in Neuroinformatics, 15(15): 723406.
F. Porrmann, et al., “Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation”, Frontiers in Neuroinformatics, vol. 15, 2021, : 723406.
Porrmann, F., Pilz, S., Stella, A., Kleinjohann, A., Denker, M., Hagemeyer, J., Rückert, U.: Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation. Frontiers in Neuroinformatics. 15, : 723406 (2021).
Porrmann, Florian, Pilz, Sarah, Stella, Alessandra, Kleinjohann, Alexander, Denker, Michael, Hagemeyer, Jens, and Rückert, Ulrich. “Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation”. Frontiers in Neuroinformatics 15.15 (2021): 723406.
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2021-10-01T07:30:59Z
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