# Sympiler: Transforming Sparse Matrix Codes by Decoupling Symbolic Analysis

arXiv: https://arxiv.org/abs/1705.06575 · HTML: https://mlsanity.com/paper/1705.06575/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2017.

## Built on by (most cited first, 4 of 4)

- [SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning](https://mlsanity.com/paper/2207.04606/) (2022)
- [SPLAT: A framework for optimised GPU code-generation for SParse reguLar ATtention](https://mlsanity.com/paper/2407.16847/) (2024)
- [Compressing Structured Tensor Algebra](https://mlsanity.com/paper/2407.13726/) (2024)
- [Reinforcement Learning-Guided Graph Transformations for SpTRSV Optimization](https://mlsanity.com/paper/2609.40159/) (2026)

## Abstract (arXiv)

Sympiler is a domain-specific code generator that optimizes sparse matrix computations by decoupling the symbolic analysis phase from the numerical manipulation stage in sparse codes. The computation patterns in sparse numerical methods are guided by the input sparsity structure and the sparse algorithm itself. In many real-world simulations, the sparsity pattern changes little or not at all. Sympiler takes advantage of these properties to symbolically analyze sparse codes at compile-time and to apply inspector-guided transformations that enable applying low-level transformations to sparse codes. As a result, the Sympiler-generated code outperforms highly-optimized matrix factorization codes from commonly-used specialized libraries, obtaining average speedups over Eigen and CHOLMOD of 3.8X and 1.5X respectively.