1) A data-driven approach to debug info quality - Emil Pedersen

2) GISel for Scalable Vectors: Expanding the Horizon - Jiahan Xie

3) The syntax dialect: creating a parser generator with MLIR - Fabian Mora-Cordero

1) A data-driven approach to debug info quality - Emil Pedersen
Debugging optimized code is frustrating: often, variables are missing. But some of these variables could be salvaged. In this talk, we will present a new analysis that detects when variables are lost in the compiler. This has two advantages: It allows to focus work on fixing optimizer passes that lose the most debug variables, and, by running it on real-world code, it also makes it easy to find concrete test cases where variable debug info is lost. We will use the work done on passes in the Swift frontend as an example, where we were able to increase the number of variables available in LLDB using this approach.

2) GISel for Scalable Vectors: Expanding the Horizon - Jiahan Xie
Discover the groundbreaking implementation of GISel for scalable vectors, targeting the RISC-V vector extension. This talk delves into the challenges and solutions of supporting scalable vector ALU and load/store instructions, offering insights and best practices for LLVM developers working on GISel for other targets.

3) The syntax dialect: creating a parser generator with MLIR - Fabian Mora-Cordero
This talk presents the syntax dialect, an MLIR dialect for formal language analysis and parser generation, and its associated tools. The syntax dialect is an MLIR dialect that can represent regular and context-free languages and parsing expression grammars (PEG). We found that using MLIR simplified the introduction of complex concepts like macros over formal languages, as we can reuse passes like function inlining to handle their intricacies. Finally, we discuss future work of creating lowerings to other MLIR dialects to be able to dynamically create and JIT lexers and parsers by using the MLIR execution engine.