Parallelization of Automatic Tuning for Hyperparameter Optimization of Pedestrian Route Prediction Applications using Machine Learning

Sorataro Fujika, Yuga Yajima, Teruo Tanaka, Akihiro Fujii, Yuka Kato, Satoshi Ohshima, Takahiro Katagiri

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

We study software automatic tuning. Automatic tuning tools using iterative one-dimensional search estimate hyperparameters of machine learning programs. Iterative one-dimensional search searches the parameter space consisting of possible values of the parameters to be tuned by repeatedly measuring and evaluating the target program. Since it takes time to train a machine learning program, estimating the optimal hyperparameters is time-consuming. Therefore, we propose a method to reduce the time required for automatic tuning by parallelization of iterative one-dimensional search. For parallelization, we use multiple job execution on a supercomputer that can utilize multiple GPUs, which is effective for machine learning. In this method, each job measures different hyperparameters. The next search point is determined by referring to the data obtained from each job. The target program is a pedestrian path prediction application. This program predicts future routes and arrival points based on past pedestrian trajectory data. The program is intended to be used in a variety of locations, and the locations and movement patterns will vary depending on the dataset used for training. We hypothesized that the estimation results of one dataset could be used for automatic tuning of another dataset, thereby reducing the time required for automatic tuning. Experimental results confirm that the parallelized iterative one-dimensional search reduces the estimation time from 89.5 hours to 4 hours compared to the sequential search. We also show that the iterative one-dimensional search efficiently investigates the point at which the performance index improves. Moreover, the hyperparameters estimated for one data set are used as the initial point for the search and automatic tuning for another data set. Compared to the results of automatic tuning with the currently used hyperparameters as the initial values, both the number of executions and execution time were reduced.

本文言語英語
ホスト出版物のタイトルProceedings of International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2023
出版社Association for Computing Machinery
ページ96-105
ページ数10
ISBN(電子版)9781450398060
DOI
出版ステータス出版済み - 2月 27 2023
外部発表はい
イベント6th International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2023 - Singapore, シンガポール
継続期間: 2月 27 20233月 2 2023

出版物シリーズ

名前ACM International Conference Proceeding Series

会議

会議6th International Conference on High Performance Computing in Asia-Pacific Region, HPC Asia 2023
国/地域シンガポール
CitySingapore
Period2/27/233/2/23

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 人間とコンピュータの相互作用
  • コンピュータ ビジョンおよびパターン認識
  • コンピュータ ネットワークおよび通信

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