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arXiv · 2306.00366

Bridging Control-Centric and Data-Centric Optimization

Tal Ben-Nun·Berke Ates·Alexandru Calotoiu·Torsten Hoefler
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Abstract

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.

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Tal Ben-Nun, Berke Ates, Alexandru Calotoiu, Torsten Hoefler. 2023-06-01. Bridging Control-Centric and Data-Centric Optimization. https://doi.org/10.1145/3579990.3580018

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