比较DL框架的抽象成本
Comparing the costs of abstraction for DL frameworks
摘要：
用于实现、训练和测试深度学习（DL）模型的高级抽象比比皆是。这些框架主要通过抽象任意神经架构的实现细节来发挥作用，从而使研究人员和工程师能够专注于设计。原则上，这种框架可以是“零成本抽象”;在实践中，它们会产生翻译和间接费用。我们研究了在DL模型的工程生命周期中的哪些点上支付的成本最高，以及是否可以减轻这些成本。我们使用PyTorch、LibTorch、TorchScript和cuDNN在代表性数据集上对代表性DL模型进行了训练、测试和评估，比较了准确性、执行时间和内存效率。
英文摘要：
High level abstractions for implementing, training, and testing Deep Learning (DL) models abound. Such frameworks function primarily by abstracting away the implementation details of arbitrary neural architectures, thereby enabling researchers and engineers to focus on design. In principle, such frameworks could be "zero-cost abstractions"; in practice, they incur translation and indirection overheads. We study at which points exactly in the engineering life-cycle of a DL model the highest costs are paid and whether they can be mitigated. We train, test, and evaluate a representative DL model using PyTorch, LibTorch, TorchScript, and cuDNN on representative datasets, comparing accuracy, execution time and memory efficiency.
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。
