Modern microprocessors are equipped with single instruction multiple data (SIMD) or vector instruction sets which allow compilers to exploit superword level parallelism (SLP), a type of fine-grained parallelism. Current SLP auto-vectorization techniques use heuristics to discover vectorization opportunities in high-level language code. These heuristics are fragile, local and typically only present one vectorization strategy that is either accepted or rejected by a cost model. We present goSLP, a novel SLP auto-vectorization framework which solves the statement packing problem in a pairwise optimal manner. Using an integer linear programming (ILP) solver, goSLP searches the entire space of statement packing opportunities for a whole function at a time, while limiting total compilation time to a few minutes. Furthermore, goSLP optimally solves the vector permutation selection problem using dynamic programming. We implemented goSLP in the LLVM compiler infrastructure, achieving a geometric mean speedup of 7.58% on SPEC2017fp, 2.42% on SPEC2006fp and 4.07% on NAS benchmarks compared to LLVM’s existing SLP auto-vectorizer.
Wed 7 Nov
|10:30 - 10:52|
Nachshon CohenEPFL, Switzerland
|10:52 - 11:15|
Benoit DalozeJKU Linz, Austria, Arie TalTechnion, Stefan MarrUniversity of Kent, Hanspeter MössenböckJKU Linz, Austria, Erez PetrankTechnionPre-print
|11:15 - 11:37|
|11:37 - 12:00|