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This paper describes our experiences creating Tornado: a practical andefficient heterogeneous programming framework for managed languages. The novelaspect of Tornado is that it turns the programming of heterogeneous systemsfrom an activity predominantly based on a priori knowledge into one based on aposteriori knowledge. Alternatively put, it simply means developers do not needto overcomplicate their code by catering for all possible eventualities.Instead, Tornado provides the ability to specialize each application for aspecific system in situ which avoids the need for it to be pre-configured bythe developer. To enable this, Tornado employs a sophisticated runtime systemthat can dynamically configure all aspects of the application - from selectingwhich parallelization scheme to apply to specifying which accelerators to use.By using this ability, the end-user, and not the developer, can transparentlymake use of any available multi-/many-core processor or hardware accelerator.  To showcase the impact of Tornado, we implement a real-world computer visionapplication and deploy it across nine accelerators without having to modify thesource code or even explicitly re-compile the application. Using dynamicconfiguration, we show that our implementation can achieve up to 124 frames persecond (FPS) - up to 166x speedup over the reference implementation. Finally,our implementation is always within 21% of a hand-written OpenCL version butavoids much of the programming tedium.

Tornado: A Practical And Efficient Heterogeneous Programming Framework For Managed Languages