“It looks like you’re writing a parallel loop”: a machine learning based parallelization assistant
Aleksandr Maramzin, Christos Vasiladiotis, Roberto Castañeda Lozano, Murray Cole, Björn Franke · 2019
Despite decades of research into parallelizing compiler technology, software parallelization remains a largely manual task where the key resource is expert time. In this paper we focus on the time-consuming task of identifying those loops in a program, which are both worthwhile and feasible to parallelize. We present a methodology and tool which make better use of expert time by guiding their effort directly towards those loops, where the largest performance gains can be expected while keeping analysis and transformation effort at a minimum.