Estimating Potential Parallelism by Data-Dependence Profiling

With the rise of multi-core consumer hardware, today’s software developers face the challenging task of refactoring existing sequential code assets with a view to exploiting modern multi-core processors’ parallel execution capabilities. While there is a growing range of methods to support parallelism in software products, a software engineer nonetheless has to decide which parts of an already existing software are worth the effort of being modified. We propose an extensible data dependence profiling framework that facilitates the estimation of a software component’s inherent potential for parallelism. Our framework is based on the Low Level Virtual Machine (LLVM) compiler infrastructure. We use LLVM to perform a static analysis of a given code and instrument the code at the intermediate representation level to record memory accesses and control flow. The information recorded, together with the result of the static analysis, is used to estimate the parallelism inherent in the program. We evaluate our tool on a benchmark covering algorithmic problems like searching, sorting and numerical methods.