Source Matching and Rewriting

TL;DR

SMR is a user-friendly, source-code-based approach for matching and rewriting idioms in MLIR, enabling automatic code raising to accelerate programs without expert intervention.

Contribution

Introduces SMR, a two-phase automaton-based DAG-matching method for idiom detection and rewriting in MLIR that does not require specialized language knowledge.

Findings

Effectively matches idioms in Fortran and C programs.

Raises idioms as BLAS calls to enhance performance.

Operates without compiler expert intervention.

Abstract

A typical compiler flow relies on a uni-directional sequence of translation/optimization steps that lower the program abstract representation, making it hard to preserve higher-level program information across each transformation step. On the other hand, modern ISA extensions and hardware accelerators can benefit from the compiler's ability to detect and raise program idioms to acceleration instructions or optimized library calls. Although recent works based on Multi-Level IR (MLIR) have been proposed for code raising, they rely on specialized languages, compiler recompilation, or in-depth dialect knowledge. This paper presents Source Matching and Rewriting (SMR), a user-oriented source-code-based approach for MLIR idiom matching and rewriting that does not require a compiler expert's intervention. SMR uses a two-phase automaton-based DAG-matching algorithm inspired by early work on tree-pattern matching. First, the idiom Control-Dependency Graph (CDG) is matched against the program's CDG to rule out code fragments that do not have a control-flow structure similar to the desired idiom. Second, candidate code fragments from the previous phase have their Data-Dependency Graphs (DDGs) constructed and matched against the idiom DDG. Experimental results show that SMR can effectively match idioms from Fortran (FIR) and C (CIL) programs while raising them as BLAS calls to improve performance.