The breakthrough in Deep Learning neural networks has transformed the use of AI and machine learning technologies for the analysis of very large experimental datasets. These datasets are typically generated by large-scale experimental facilities at national laboratories. In the context of science, scientific machine learning focuses on training machines to identify patterns, trends, and anomalies to extract meaningful scientific insights from such datasets. With a new generation of experimental facilities, the rate of data generation and the scale of data volumes will increasingly require the use of more automated data analysis. At present, identifying the most appropriate machine learning algorithm for the analysis of any given scientific dataset is still a challenge for scientists. This is due to many different machine learning frameworks, computer architectures, and machine learning models. Historically, for modelling and simulation on HPC systems such problems have been addressed through benchmarking computer applications, algorithms, and architectures. Extending such a benchmarking approach and identifying metrics for the application of machine learning methods to scientific datasets is a new challenge for both scientists and computer scientists. In this paper, we describe our approach to the development of scientific machine learning benchmarks and review other approaches to benchmarking scientific machine learning.
翻译:深层学习神经网络的突破改变了使用人工智能和机器学习技术分析非常庞大的实验数据集的情况。这些数据集通常由国家实验室的大规模实验设施生成。在科学方面,科学机器学习侧重于培训机器,以辨别模式、趋势和异常现象,从这些数据集中获取有意义的科学见解。随着新一代实验设施的发展,数据生成速度和数据量的规模将日益要求使用更自动化的数据分析。目前,确定用于分析任何特定科学数据集的最适当的机器学习算法,对科学家来说仍然是一项挑战。这是由于许多不同的机器学习框架、计算机架构和机器学习模型。从历史上看,这些问题是通过计算机应用基准、算法和结构来模拟高电电电离层系统的模拟和模拟的。扩大这种基准方法并确定将机器学习方法应用于科学数据集的衡量标准对科学家和计算机科学家来说都是一项新的挑战。我们在本文件中描述了我们开发科学机器学习基准的方法,并审查了为科学机器学习制定基准的其他方法。