# Bridging Control-Centric and Data-Centric Optimization

arXiv: https://arxiv.org/abs/2306.00366 · HTML: https://mlsanity.com/paper/2306.00366/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2023.

## Built on by (most cited first, 1 of 1)

- [Optimizing FDTD Solvers for Electromagnetics: A Compiler-Guided Approach with High-Level Tensor Abstractions](https://mlsanity.com/paper/2504.09118/) (2025)

## Abstract (arXiv)

With the rise of specialized hardware and new programming languages, code optimization has shifted its focus towards promoting data locality. Most production-grade compilers adopt a control-centric mindset - instruction-driven optimization augmented with scalar-based dataflow - whereas other approaches provide domain-specific and general purpose data movement minimization, which can miss important control-flow optimizations. As the two representations are not commutable, users must choose one over the other. In this paper, we explore how both control- and data-centric approaches can work in tandem via the Multi-Level Intermediate Representation (MLIR) framework. Through a combination of an MLIR dialect and specialized passes, we recover parametric, symbolic dataflow that can be optimized within the DaCe framework. We combine the two views into a single pipeline, called DCIR, showing that it is strictly more powerful than either view. On several benchmarks and a real-world application in C, we show that our proposed pipeline consistently outperforms MLIR and automatically uncovers new optimization opportunities with no additional effort.