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| Hauptseite > Publikationsdatenbank > Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation > print |
| Hauptseite > Publikationsdatenbank > Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation > print |
| 001 | 902918 | ||
| 005 | 20240313103132.0 | ||
| 024 | 7 | _ | |a 10.3389/fninf.2021.723406 |2 doi |
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| 037 | _ | _ | |a FZJ-2021-04673 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Porrmann, Florian |0 P:(DE-HGF)0 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation |
| 260 | _ | _ | |a Lausanne |c 2021 |b Frontiers Research Foundation |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Pilz, Sarah |0 P:(DE-HGF)0 |b 1 |
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| 700 | 1 | _ | |a Kleinjohann, Alexander |0 P:(DE-Juel1)176920 |b 3 |
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| 700 | 1 | _ | |a Rückert, Ulrich |0 P:(DE-HGF)0 |b 6 |
| 773 | _ | _ | |a 10.3389/fninf.2021.723406 |g Vol. 15, p. 723406 |0 PERI:(DE-600)2452979-5 |p 723406 |t Frontiers in neuroinformatics |v 15 |y 2021 |x 1662-5196 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/902918/files/fninf-15-723406.pdf |y OpenAccess |
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