Discovering Parallelization Opportunities in Sequential Programs — A Closer-to-Complete Solution

The stagnation of single-core performance leaves application developers with software parallelism as the only option to further benefit from Moore’s Law. However, in view of the complexity of writing parallel programs, the parallelization of myriads of sequential legacy programs presents a serious economic challenge. A key task in this process is the identification of suitable parallelization targets in the source code. Reversing the idea underlying data-race detectors, we show how dependency profiling can be used to automatically identify potential parallelism in sequential programs of realistic size. In comparison to earlier approaches, our work combines a unique set of features that make it superior in terms of functionality: It not only (i) detects available parallelism with high accuracy but also (ii) identifies the parts of the code that can run in parallel—even if they are spread widely across the code, (iii) ranks paralleization opportunities according to the speedup expected for the entire program, while (iv) maintaining competitive overhead both in terms of time and memory.