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- W4321496220 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." @default.
- W4321496220 created "2023-02-23" @default.
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- W4321496220 date "2023-02-17" @default.
- W4321496220 modified "2023-09-26" @default.
- W4321496220 title "Bridging Control-Centric and Data-Centric Optimization" @default.
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- W4321496220 doi "https://doi.org/10.1145/3579990.3580018" @default.
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