Skyline：用于深度神经网络训练的交互式编辑器内计算性能分析
Skyline: Interactive In-Editor Computational Performance Profiling for Deep Neural Network Training
摘要：
训练最先进的深度神经网络（DNN）是一个计算昂贵且耗时的过程，这激励深度学习开发人员调试其DNN的计算性能。然而，有效地执行这种调试需要对底层软件和硬件系统有深入的了解--这是典型的深度学习开发人员可能不具备的。为了帮助弥合这一差距，我们提出了Skyline：一种用于DNN培训的新交互式工具，支持编辑器内的计算性能分析，可视化和调试。Skyline的主要贡献在于它利用DNN训练的特殊计算属性来提供（i）交互式性能预测和可视化，以及（ii）直接可操纵的可视化，当拖动时，会改变代码中的批量大小。作为一个编辑器内工具，Skyline允许用户利用这些诊断功能在开发过程中调试DNN的性能。对Skyline的探索性定性用户研究产生了有希望的结果;所有参与者都发现Skyline有用且易于使用。
英文摘要：
Training a state-of-the-art deep neural network (DNN) is a computationally-expensive and time-consuming process, which incentivizes deep learning developers to debug their DNNs for computational performance. However, effectively performing this debugging requires intimate knowledge about the underlying software and hardware systems---something that the typical deep learning developer may not have. To help bridge this gap, we present Skyline: a new interactive tool for DNN training that supports in-editor computational performance profiling, visualization, and debugging. Skyline's key contribution is that it leverages special computational properties of DNN training to provide (i) interactive performance predictions and visualizations, and (ii) directly manipulatable visualizations that, when dragged, mutate the batch size in the code. As an in-editor tool, Skyline allows users to leverage these diagnostic features to debug the performance of their DNNs during development. An exploratory qualitative user study of Skyline produced promising results; all the participants found Skyline to be useful and easy to use.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
