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

Abstract

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.