אמיר חאג' אלי ( אונ' קליפורניה בברקלי)
יום רביעי, 8.1.2020, 15:30
חדר 815, בניין מאייר, הפקולטה להנדסת חשמל
Compilers are designed today to use fixedAbstract -cost models that are based on heuristics to make different code optimizations. However, these models are unable to capture the data dependency, the computation graph, or the organization of instructions. Therefore, with these models compilers achieve suboptimal performance. In his talk, Ameer will show how to use deep reinforcement learning to overcome such hard compiler challenges. This includes the NP-Hard compiler phase ordering challenge (AutoPhase, FCCM2019, SysML2020) and automatically tuning compiler pragmas such as vectorization (NeuroVectorizer, CGO2020).