Performance optimization by dynamic code transformation

Abstract

Even parts of a program that are sequential or just inherently difficult to parallelize can be optimized for ILP. For instance, eliminating loop overheads and potential pipeline stalls from control flow can alleviate performance bottlenecks. Unfortunately, static compilation is limited in the extent to which it can identify opportunities to apply such optimizations. Generating code dynamically at run time, however, create much more efficient applications by usin information not available at compile time. We demonstrate our approach on a sparse-matrix PET scan code by aggressive unrolling loops and specializing code via dynamic code generation. We leverage task-level parallelism by having an auxiliary processor core concurrently generate code and feed it to the core executing the application. Our approach to fast code generation leverages patching and concatenating prepared code skeletons.