Domain-Specific Multi-Level IR Rewriting for GPU
Citations Over TimeTop 21% of 2021 papers
Abstract
Most compilers have a single core intermediate representation (IR) (e.g., LLVM) sometimes complemented with vaguely defined IR-like data structures. This IR is commonly low-level and close to machine instructions. As a result, optimizations relying on domain-specific information are either not possible or require complex analysis to recover the missing information. In contrast, multi-level rewriting instantiates a hierarchy of dialects (IRs), lowers programs level-by-level, and performs code transformations at the most suitable level. We demonstrate the effectiveness of this approach for the weather and climate domain. In particular, we develop a prototype compiler and design stencil- and GPU-specific dialects based on a set of newly introduced design principles. We find that two domain-specific optimizations (500 lines of code) realized on top of LLVM’s extensible MLIR compiler infrastructure suffice to outperform state-of-the-art solutions. In essence, multi-level rewriting promises to herald the age of specialized compilers composed from domain- and target-specific dialects implemented on top of a shared infrastructure.
Related Papers
- → Domain-Specific Multi-Level IR Rewriting for GPU: The Open Earth Compiler for GPU-accelerated Climate Simulation(2021)60 cited
- → Integrating a functional pattern-based IR into MLIR(2021)8 cited
- Realizing Domain-specific Compiler Optimizations via Generic Programming(2008)
- → Enabling Retargetable Optimizing Compilers for Quantum Accelerators via a Multi-Level Intermediate Representation(2021)