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AI-SEPS 2019
Sun 20 - Fri 25 October 2019 Athens, Greece
co-located with SPLASH 2019
Tue 22 Oct 2019 09:00 - 09:25 at Room 2B - Papers Chair(s): Yukinori Sato

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. We have developed a novel parallelization assistant that provides programmers with a ranking of all loops in a program based on their overall merit. For each loop this metric combines its potential contribution to speedup and an estimated probability for its successful parallelization. This probability is predicted using a machine learning model, which has been trained, validated, and tested on 1415 labelled loops, achieving a prediction accuracy greater than 90%. We have evaluated our parallelization assistant against sequential C applications from the SNU NAS benchmark suite. We show that our novel methodology achieves parallel performance levels comparable to those from expert programmers while requiring less expert time. On average, our assistant reduces the number of lines of code that have to be inspected manually before reaching expert-level parallel speedup by 20%.

Tue 22 Oct

seps-2019-papers
09:00 - 10:30: AI-SEPS - Papers at Room 2B
Chair(s): Yukinori SatoToyohashi University of Technology
seps-2019-papers09:00 - 09:25
Talk
Aleksandr MaramzinUniversity of Edinburgh, Christos VasiladiotisUniversity of Edinburgh, Roberto Castañeda LozanoUniversity of Edinburgh, Murray ColeUniversity of Edinburgh, Björn FrankeUniversity of Edinburgh, UK
DOI
seps-2019-papers09:25 - 09:40
Talk
Yannic FischlerTU Darmstadt, Jan-Patrick LehrGraduate School of Computational Engineering, TU Darmstadt, Christian BischofScientific Computing, TU Darmstadt, Matthäus Magnus KiehnTU Darmstadt
DOI