Dependence-based code transformation for coarse-grained parallelism

Multicore architectures are becoming more common today. Many software products implemented sequentially have failed to exploit the potential parallelism of multicore architectures. Significant re-engineering and refactoring of existing software is needed to support the use of new hardware features. Due to the high cost of manual transformation, an automated approach to transforming existing software and taking advantage of multicore architectures would be highly beneficial. We propose a novel auto-parallelization approach, which integrates data-dependence profiling, task parallelism extraction and source-to-source transformation. Coarse-grained task parallelism is detected based on a concept called Computational Unit(CU). We use dynamic profiling information to gather control- and data-dependences among tasks and generate a task graph. In addition, we develop a source-to-source transformation tool based on LLVM, which can perform high-level code restructuring. It transforms the generated task graph with loop parallelism and task parallelism of sequential code into parallel code using Intel Threading Building Blocks (TBB). We have evaluated NAS Parallel Benchmark applications, three applications from PARSEC benchmark suite, and real world applications. The obtained results confirm that our approach is able to achieve promising performance with minor user interference. The average speedups of loop parallelization and task parallelization are 3.12x and 9.92x respectively.