Runtime analysis of application binaries for function level parallelism potential using QEMU

Efficacy of automatic parallelization techniques that rely on source code analysis alone is often limited due to lack of information about runtime characteristics of target applications. In order to exploit runtime application behavior for its parallelization, we need: (1) tools/techniques for dynamic instrumentation and profiling; and (2) a methodology to identify areas of application that are amenable for explicit and speculative parallelization. In this paper, we present an infrastructure that provides above-mentioned facilities to analyze ELF binaries in an emulated runtime environment. The infrastructure, which is implemented as an extension to quick emulator (QEMU), includes a profiling mechanism to capture runtime behavior of an application and an inter-function dependence metric for quantitatively measure the potential for function level parallelism. The dependence metric is an extension of data dependence densities effort [7]. We ran sequential versions of NAS benchmarks through this infrastructure to determine their function level parallelization potential. Resulting data can be consumed for manual parallelization efforts as well as for automated parallelization through compiler feedback during build process.