- Pattern Matching, Transformation and Code Replacement using LLVM's Polly library
- How to Write a Scalable Compiler for an Error-Prone Quantum Computer
- Optimizing FDTD Solvers for Electromagnetics Using MLIR Across Multiple Hardware Platforms
1) Pattern Matching, Transformation and Code Replacement using LLVM's Polly library - Benedikt Huber
Multidimensional Match, Transform and Replace (MMTR), is an extension to LLVM's Polly library, capable of matching, transforming and replacing nested loops using the polyhedral model. According to a user defined, target specific pattern, MMTR identifies nested loops that are semantically equivalent to an optimized target implementation. On a successful match, it transforms the nested loop in such a way that it can be replaced by a call to this optimized target implementation.
2) How to Write a Scalable Compiler for an Error-Prone Quantum Computer - Kim Worrall
Quantum computers have a problem - they are susceptible to errors which can destroy the entire computation. Error correction will make quantum computers feasible, but the architecture and software needed is multi-level, distributed, data intensive, time critical, and crucially not yet integrated into a compilation pipeline. This talk presents our effort to bring together the disparate parts of the quantum ecosystem into an error-correction aware, MLIR compatible framework. In particular, we emphasise how lessons from the MLIR and LLVM community are crucial to the frameworks development, and invite the audience to get involved with quantum computing.
3) Optimizing FDTD Solvers for Electromagnetics Using MLIR Across Multiple Hardware Platforms - Yifei He
The Finite-Difference Time-Domain (FDTD) method is essential for computational electromagnetics but is computationally intensive. This talk explores how MLIR optimizations—loop tiling, fusion, and vectorization—significantly accelerate FDTD simulations. We showcase MLIR’s potential for efficient, portable FDTD solvers across Intel, AMD, ARM CPUs, and GPUs.