Title: An Open-Source-First Approach: Multi-Level Compiler Design for AI and HPC
Date: 2026/05/26 14:00-15:00
Location: R210, CSIE
Speaker: Dr. Tobias Grosser, Associate Professor in Compiler Design, University of Cambridge
Host: Prof. Shih-Wei Li
Abstract:
Writing compilers yourself (or understanding them in great detail) has become mission-critical for performance engineers in HPC and AI. HPC engineers still rely on high-performance C++ or Fortran compilers, OpenMP and MPI, and CUDA to program their applications. Yet, highly specialized hardware accelerators from Tenstorrent, Cerebras, and Nextsilicon, as well as higher-level programming models from NVIDIA, have widened the programming surface one needs to master to attain peak performance. While a plethora of new programming models are used to increase accessibility, designing a custom domain-specific compiler stack is surprisingly often an impactful option. In the following talk, I share my experience building multi-level compilers, from HPC to AI, within the MLIR ecosystem, covering MLIR’s GPU dialect and its use for scientific computing, MLIR’s first-class MPI support, our work on targeting Cerbera’s Waverscale engine, and our most recent effort of opening the LLVM backend at the ISA-level using MLIR – often using Python to accelerate development. I then discuss how our open-source-first research approach enabled us to deliver practically informed research that scales to real workloads, while contributing ideas and code back to the wider open-source ecosystem and helping to make compiler design a powerful tool for HPC and beyond.
Bio:
Tobias Grosser is an Associate Professor at the University of Cambridge. Before, he worked as a Reader at the University of Edinburgh, as an Ambizione Fellow at ETH Zurich, and as a Google PhD Fellow at INRIA/Paris IV/ENS Paris. Tobias and his research group have a decade-long history of contributing to the LLVM ecosystem. Tobias developed polyhedral loop optimizations in LLVM/Polly, worked on hardware design with LLHD/CIRCT, and co-developed the xDSL Python-Native MLIR-style compiler framework, which is used to target low-power AI accelerators or quantum computers. Over the last few years, he started to look into formal methods in the context of the Lean ITP.