Automatic Loop Parallelization via Compiler Guided Refactoring

For many parallel applications, performance relies not on instruction-level parallelism, but on loop-level parallelism. Unfortunately, many modern applications are written in ways that obstruct automatic loop parallelization. Since we cannot identify sufficient parallelization opportunities for these codes in a static, off-line compiler, we developed an interactive compilation feedback system that guides the programmer in iteratively modifying application source, thereby improving the compiler’s ability to generate loop-parallel code.

We use this compilation system to modify two sequential benchmarks, finding that the code parallelized in this way runs up to 8.3 times faster on an octo-core Intel Xeon 5570 system and up to 12.5 times faster on a quad-core IBM POWER6 system. Benchmark performance varies significantly between the systems.

This suggests that semi-automatic parallelization should be combined with target-specific optimizations. Furthermore, comparing the first benchmark to hand-parallelized, hand-optimized pthreads and OpenMP versions, we find that code generated using our approach typically outperforms the pthreads code (within 93-339%).

It also performs competitively against the OpenMP code (within 75-111%). The second benchmark outperforms hand-parallelized and optimized OpenMP code (within 109-242%).