Optimizing Sparse Tensor Compilation for Sparse Output

doi:10.1145/3771775.3786267

2026-01-01

PRE

Shideh Hashemian *

Michael F. P. O’Boyle

Amir Shaikhha

DOI：10.1145/3771775.3786267

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摘要

En 中文

稀疏张量代数在许多科学和工程应用中发挥着重要作用，但现有稀疏库和编译器在输出张量为稀疏时面临挑战。基于数组的存储格式（如CSR）需要昂贵的内存重新分配，并依赖中间张量（工作区）来处理稀疏散布到输出，这限制了性能和可扩展性。我们介绍了一种新方法，采用我们提出的灵活映射基存储格式直接支持稀疏散布到输出，而无需额外的工作区。我们的系统随后应用代码和存储特定的优化以最大化效率。在一系列内核和数据集上的实验结果证明，与最先进的编译器相比平均加速8.06倍，与稀疏张量库相比平均加速5.28倍。

Keyword:

Sparse

Tensors

Sparse Data Structures

Tensor Algebra

期刊

PROCEEDINGS OF THE 35TH ACM SIGPLAN INTERNATIONAL CONFERENCE ON COMPILER CONSTRUCTION, CC 2026

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机构

University of Edinburgh

学者数:

5.2W

论文数: 4.6W

被引数: 71

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