Hybrid online/offline optimization of Application Binaries

Abstract

Long-running parallel applications suffer from performance limitations particularly due to inefficiencies in accessing memory. Dynamic optimizations, i.e., optimizations performed at execution time, provide opportunities not available at compile or link time to improve performance and remove bottlenecks for the current execution. In particular, they enable one to apply transformations to tune the performance for a particular execution instance. This can potentially include effects of the environment, input values as well as be able to optimize code from other sources like pre-compiled libraries and code from mixed-language sources. This thesis presents design and implementation of components of a dynamic optimizing system for long-running parallel applications that use dynamic binary rewriting. The system uses a hybrid online/offline model to collect a memory profile that guides the choice of functions to be optimized. We describe the design and implementation of a module that enables optimization of a desired function from the executable, i.e., without relying on the source code. We also present the module that enables hot swapping of code of an executing application. Dynamic binary rewriting is used to hot-swap the bottleneck function with an optimized function while the application is still executing. Binary manipulation is used in two ways - first to collect a memory profile through instrumentation to identify bottleneck functions and then to control hot-swapping of code using program transformation. We show experiments as a proof of concept for implementations of remaining components of the framework and for validation of existing modules.