The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Strategi pelaksanaan yang cekap untuk mempercepatkan algoritma pengelompokan berkualiti tinggi dibangunkan berdasarkan unit pemprosesan grafik tujuan am (GPGPU) dalam kerja ini. Di antara pelbagai algoritma pengelompokan, model campuran Gaussian (GMM) yang canggih dengan menganggar parameter melalui mekanisme Bayesian (VB) variasi dijalankan kerana prestasinya yang unggul. Memandangkan metodologi VB-GMM haus pengiraan, GPGPU digunakan untuk menjalankan pengiraan matriks besar-besaran. Untuk memindahkan skim berorientasikan CPU konvensional VB-GMM ke platform GPGPU dengan cekap, keseluruhan aliran migrasi dengan tiga belas peringkat dibentangkan secara terperinci. Skim kerjasama CPU-GPGPU, susunan semula pelaksanaan dan pengoptimuman akses memori dicadangkan untuk mengoptimumkan penggunaan GPGPU dan memaksimumkan kelajuan pengelompokan. Lima jenis aplikasi dunia sebenar bersama set data yang berkaitan diperkenalkan untuk pengesahan silang. Daripada keputusan percubaan, kemungkinan melaksanakan algoritma VB-GMM oleh GPGPU disahkan dengan faedah praktikal. Penghijrahan GPGPU yang dicadangkan mencapai kelajuan maksimum 192x ganda. Tambahan pula, ia berjaya mengenal pasti bilangan kluster yang betul, yang hampir tidak dijalankan oleh EM-algotihm.
Hiroki NISHIMOTO
Nara Institute of Science and Technology
Renyuan ZHANG
Nara Institute of Science and Technology
Yasuhiko NAKASHIMA
Nara Institute of Science and Technology
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Salinan
Hiroki NISHIMOTO, Renyuan ZHANG, Yasuhiko NAKASHIMA, "GPGPU Implementation of Variational Bayesian Gaussian Mixture Models" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 3, pp. 611-622, March 2022, doi: 10.1587/transinf.2021EDP7121.
Abstract: The efficient implementation strategy for speeding up high-quality clustering algorithms is developed on the basis of general purpose graphic processing units (GPGPUs) in this work. Among various clustering algorithms, a sophisticated Gaussian mixture model (GMM) by estimating parameters through variational Bayesian (VB) mechanism is conducted due to its superior performances. Since the VB-GMM methodology is computation-hungry, the GPGPU is employed to carry out massive matrix-computations. To efficiently migrate the conventional CPU-oriented schemes of VB-GMM onto GPGPU platforms, an entire migration-flow with thirteen stages is presented in detail. The CPU-GPGPU co-operation scheme, execution re-order, and memory access optimization are proposed for optimizing the GPGPU utilization and maximizing the clustering speed. Five types of real-world applications along with relevant data-sets are introduced for the cross-validation. From the experimental results, the feasibility of implementing VB-GMM algorithm by GPGPU is verified with practical benefits. The proposed GPGPU migration achieves 192x speedup in maximum. Furthermore, it succeeded in identifying the proper number of clusters, which is hardly conducted by the EM-algotihm.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7121/_p
Salinan
@ARTICLE{e105-d_3_611,
author={Hiroki NISHIMOTO, Renyuan ZHANG, Yasuhiko NAKASHIMA, },
journal={IEICE TRANSACTIONS on Information},
title={GPGPU Implementation of Variational Bayesian Gaussian Mixture Models},
year={2022},
volume={E105-D},
number={3},
pages={611-622},
abstract={The efficient implementation strategy for speeding up high-quality clustering algorithms is developed on the basis of general purpose graphic processing units (GPGPUs) in this work. Among various clustering algorithms, a sophisticated Gaussian mixture model (GMM) by estimating parameters through variational Bayesian (VB) mechanism is conducted due to its superior performances. Since the VB-GMM methodology is computation-hungry, the GPGPU is employed to carry out massive matrix-computations. To efficiently migrate the conventional CPU-oriented schemes of VB-GMM onto GPGPU platforms, an entire migration-flow with thirteen stages is presented in detail. The CPU-GPGPU co-operation scheme, execution re-order, and memory access optimization are proposed for optimizing the GPGPU utilization and maximizing the clustering speed. Five types of real-world applications along with relevant data-sets are introduced for the cross-validation. From the experimental results, the feasibility of implementing VB-GMM algorithm by GPGPU is verified with practical benefits. The proposed GPGPU migration achieves 192x speedup in maximum. Furthermore, it succeeded in identifying the proper number of clusters, which is hardly conducted by the EM-algotihm.},
keywords={},
doi={10.1587/transinf.2021EDP7121},
ISSN={1745-1361},
month={March},}
Salinan
TY - JOUR
TI - GPGPU Implementation of Variational Bayesian Gaussian Mixture Models
T2 - IEICE TRANSACTIONS on Information
SP - 611
EP - 622
AU - Hiroki NISHIMOTO
AU - Renyuan ZHANG
AU - Yasuhiko NAKASHIMA
PY - 2022
DO - 10.1587/transinf.2021EDP7121
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2022
AB - The efficient implementation strategy for speeding up high-quality clustering algorithms is developed on the basis of general purpose graphic processing units (GPGPUs) in this work. Among various clustering algorithms, a sophisticated Gaussian mixture model (GMM) by estimating parameters through variational Bayesian (VB) mechanism is conducted due to its superior performances. Since the VB-GMM methodology is computation-hungry, the GPGPU is employed to carry out massive matrix-computations. To efficiently migrate the conventional CPU-oriented schemes of VB-GMM onto GPGPU platforms, an entire migration-flow with thirteen stages is presented in detail. The CPU-GPGPU co-operation scheme, execution re-order, and memory access optimization are proposed for optimizing the GPGPU utilization and maximizing the clustering speed. Five types of real-world applications along with relevant data-sets are introduced for the cross-validation. From the experimental results, the feasibility of implementing VB-GMM algorithm by GPGPU is verified with practical benefits. The proposed GPGPU migration achieves 192x speedup in maximum. Furthermore, it succeeded in identifying the proper number of clusters, which is hardly conducted by the EM-algotihm.
ER -