Estimating and Exploiting Potential Parallelism by Source-Level Dependence Profiling

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Manual parallelization of programs is known to be difficult and error-prone, and there are currently few ways to measure the amount of potential parallelism in the original sequential code.

We present an extension of Embla, a Valgrind-based dependence profiler that links dynamic dependences back to source code. This new tool estimates potential task-level parallelism in a sequential program and helps programmers exploit it at the source level. Using the popular fork-join model, our tool provides a realistic estimate of potential speed-up for parallelization with frameworks like Cilk, TBB or OpenMP 3.0 . Estimates can be given for several different parallelization models, varying in programmer effort and capabilities required of the underlying implementation. Our tool also outputs source-level dependence information to aid the parallelization of programs with lots of inherent parallelism, as well as critical paths to suggest algorithmic rewrites of programs with little of it.

We validate our claims by running our tool over serial elisions of sample Cilk programs, finding additional inherent parallelism not exploited by the Cilk code, as well as over serial C benchmarks where the profiling results suggest parallelism-enhancing algorithmic rewrites.