English

mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis

Programming Languages 2023-10-09 v1 Computation and Language Distributed, Parallel, and Cluster Computing Performance

Abstract

MLIR is an emerging compiler infrastructure for modern hardware, but existing programs cannot take advantage of MLIR's high-performance compilation if they are described in lower-level general purpose languages. Consequently, to avoid programs needing to be rewritten manually, this has led to efforts to automatically raise lower-level to higher-level dialects in MLIR. However, current methods rely on manually-defined raising rules, which limit their applicability and make them challenging to maintain as MLIR dialects evolve. We present mlirSynth -- a novel approach which translates programs from lower-level MLIR dialects to high-level ones without manually defined rules. Instead, it uses available dialect definitions to construct a program space and searches it effectively using type constraints and equivalences. We demonstrate its effectiveness \revi{by raising C programs} to two distinct high-level MLIR dialects, which enables us to use existing high-level dialect specific compilation flows. On Polybench, we show a greater coverage than previous approaches, resulting in geomean speedups of 2.5x (Intel) and 3.4x (AMD) over state-of-the-art compilation flows for the C programming language. mlirSynth also enables retargetability to domain-specific accelerators, resulting in a geomean speedup of 21.6x on a TPU.

Keywords

Cite

@article{arxiv.2310.04196,
  title  = {mlirSynth: Automatic, Retargetable Program Raising in Multi-Level IR using Program Synthesis},
  author = {Alexander Brauckmann and Elizabeth Polgreen and Tobias Grosser and Michael F. P. O'Boyle},
  journal= {arXiv preprint arXiv:2310.04196},
  year   = {2023}
}
R2 v1 2026-06-28T12:42:30.942Z