推动并行深度学习的边界--一种实用的方法
Pushing the boundaries of parallel Deep Learning -- A practical approach
University of Torino（都灵大学）；Noesis Solutions NV（Noesis Solutions NV公司）；Pacific Northwest National Lab.（太平洋西北国家实验室）
摘要：
这项工作旨在评估数据并行深度神经网络训练的最新技术水平，试图确定潜在的研究轨道，以提高性能。此外，它提出了一个实用的C++库的设计，致力于实现和统一当前最先进的并行训练方法，在一个性能意识的框架，允许用户探索新的策略，而不会显着偏离其通常的工作流程。
英文摘要：
This work aims to assess the state of the art of data parallel deep neural network training, trying to identify potential research tracks to be exploited for performance improvement. Beside, it presents a design for a practical C++ library dedicated at implementing and unifying the current state of the art methodologies for parallel training in a performance-conscious framework, allowing the user to explore novel strategies without departing significantly from its usual work-flow.
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。
