论文标题
对MLIR中稀疏张量计算的编译器支持
Compiler Support for Sparse Tensor Computations in MLIR
论文作者
论文摘要
稀疏张量在科学，工程，机器学习和数据分析中出现。在此类张量上操作的程序可以利用稀疏性来减少存储要求和计算时间。但是，手动开发和维护稀疏软件是一项复杂且容易出错的任务。因此，我们建议将稀疏性视为张量的属性，而不是乏味的实现任务，然后让稀疏编译器从计算的稀疏 - 不合Snostic定义自动生成稀疏的代码。本文讨论将此想法融入MLIR。
Sparse tensors arise in problems in science, engineering, machine learning, and data analytics. Programs that operate on such tensors can exploit sparsity to reduce storage requirements and computational time. Developing and maintaining sparse software by hand, however, is a complex and error-prone task. Therefore, we propose treating sparsity as a property of tensors, not a tedious implementation task, and letting a sparse compiler generate sparse code automatically from a sparsity-agnostic definition of the computation. This paper discusses integrating this idea into MLIR.
