Universal Instruction Selection
Speaker: Gabriel Hjort Blindell
Gabriel Hjort Blindell graduated with a Master in Computer Science from KTH in 2012, and started his doctoral studies at KTH the same year. Currently his research concerns compiler development - more specifically, instruction selection - using constraint programming, which is a method for combinatorial optimization.
Instruction selection implements a program under compilation by selecting processor instructions and has tremendous impact on the performance of the code generated by a compiler. In this talk, I will introduce a graph-based universal representation that unifies data and control flow for both programs and processor instructions. The representation is the essential prerequisite for a constraint model for instruction selection introduced in this paper. The model is demonstrated to be expressive in that it supports many processor features that are out of reach of state-of-the-art approaches, such as advanced branching instructions, multiple register banks, and SIMD instructions. The resulting model can be solved for small to medium size input programs and sophisticated processor instructions and is competitive with LLVM in code quality. Model and representation are significant due to their expressiveness and their potential to be combined with models for other code generation tasks.