Aggressive code optimization on the mobile environment is a difficult endeavor. Billions of users rely on mobile devices for their daily computing tasks. Yet, they mostly run poorly optimized code, under-utilizing their already limited processing and energy resources. Existing optimization approaches, like iterative compilation, perform well in other domains but fall short on the mobile environment. They either rely on representative inputs that are hard to reconstruct, or expose users to slowdowns and crashes.
An ideal solution must be able to perform an optimization search by repeatedly evaluating different optimization decisions on the same input. That input should be representative of actual user usage without requiring developers to artificially create it. Finally, users should never be exposed to slow or crashing evaluations, a quite common side-effect of iterative compilation. This thesis presents a novel approach with all above in mind, bringing aggressive code optimization to the mobile environment.
With a transparent capture mechanism, real user inputs can be stored. This mechanism is infrequently invoked and remains unnoticeable from the users. A single capture is enough to enable offline, input-driven code optimization. It supports C functions as well as code regions of interactive Android applications. It allows controlling the timing and frequency of captures, it bails out on imminent high-impact runtime events, and excludes from captures some immutable data.
A replay-based evaluation mechanism is able to repeatedly restore a captured input while changing the underlying code. For C programs, it employs compile and link-time strategies to consistently work despite code transformations. For Android apps, a novel mechanism was developed, able to replay using different code types. These are the original Android-compiled code, interpretation, and LLVM-generated code. Additionally, it works well even in the presence of memory-shuffling security mechanisms.
Capture and replay is fused into an iterative compilation system that uses offline, replay-based evaluations. Initially, real inputs are captured online, without noticeably affecting the users. For C and interactive apps, captures required on average 2ms and 15ms respectively. Then, an optimization search is performed by repeatedly replaying the inputs using different code transformations. As this happens offline, any crashing or erroneous executions are not affecting the users. C programs became 29% faster using a random search, while interactive apps became 44% faster using a genetic algorithm and a novel Android backend based on LLVM. Finally, with crowd-sourcing, the offline evaluation effort was significantly accelerated. Specifically, for the user with the highest workload the search accelerated by 7 times
