# Compiler Support for Sparse Tensor Computations in MLIR

arXiv: https://arxiv.org/abs/2202.04305 · HTML: https://mlsanity.com/paper/2202.04305/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2022.

## Built on by (most cited first, 8 of 8)

- [SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning](https://mlsanity.com/paper/2207.04606/) (2022)
- [JaxPruner: A concise library for sparsity research](https://mlsanity.com/paper/2304.14082/) (2023)
- [UniSparse: An Intermediate Language for General Sparse Format Customization](https://mlsanity.com/paper/2403.05802/) (2024)
- [STen: Productive and Efficient Sparsity in PyTorch](https://mlsanity.com/paper/2304.07613/) (2023)
- [IR Design for Application-Specific Natural Language: A Case Study on Traffic Data](https://mlsanity.com/paper/2307.06983/) (2023)
- [Scorch: A Library for Sparse Deep Learning](https://mlsanity.com/paper/2405.16883/) (2024)
- [FuseFlow: A Fusion-Centric Compilation Framework for Sparse Deep Learning on Streaming Dataflow](https://mlsanity.com/paper/2511.04768/) (2025)
- [RISC-V and machine learning: a survey](https://mlsanity.com/paper/2609.20677/) (2026)

## Abstract (arXiv)

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.