论文标题
张量数据散射和切片定理的不可能
Tensor Data Scattering and the Impossibility of Slicing Theorem
论文作者
论文摘要
本文提出了一种代表稀疏张量的标准方法。建立了用于各种深度学习框架中使用的张量数据散射方法的广泛理论框架。本文介绍了一个定理，该定理对于性能分析和用于实施数据散射的加速器优化非常重要。该定理显示了切片的不可能发生在Tenser数据散射中。提供了稀疏性测量公式，可以有效地表明稀疏张量的存储效率以及使用它的可能性。源代码（包括CUDA代码）在相关的开源项目中提供。
This paper proposes a standard way to represent sparse tensors. A broad theoretical framework for tensor data scattering methods used in various deep learning frameworks is established. This paper presents a theorem that is very important for performance analysis and accelerator optimization for implementing data scattering. The theorem shows how the impossibility of slicing happens in tenser data scattering. A sparsity measuring formula is provided, which can effectively indicate the storage efficiency of sparse tensor and the possibility of parallelly using it. The source code, including CUDA code, is provided in a related open-source project.
