tions in a tablet with Windows 8 and Internet Explorer 11 increased performance by, on average, 5 times, while running in a desktop with
Windows 7 and Firefox decreased performance by 20%. Such a scenario demands a radical new solution for the traditional compiler optimiza-
ow. This paper proposes collecting web clients performance data to build a crowdsourced compiler
ag suggestion system in the cloud that
helps the compiler perform the appropriate optimizations for each client platform. Since this information comes from crowdsourcing rather than
manual investigations, fruitless or harmful optimizations are automatically discarded. Our approach is based on live measurements done while
clients use the application on real platforms, proposing a new paradigm on how optimizations are tested.