Integrating profile-driven parallelism detection and machine-learning-based mapping
ACM Transactions on Architecture and Code Optimization (TACO)
Compiler-based auto-parallelization is a much studied area, but has yet to find wide-spread application. This is largely due to the poor identification and exploitation of application parallelism, resulting in disappointing performance far below that which a skilled expert programmer could achieve. We have identified two weaknesses in traditional parallelizing compilers and propose a novel, integrated approach, resulting in significant performance improvements of the generated parallel code.
... ng profile-driven parallelism detection we overcome the limitations of static analysis, enabling the identification of more application parallelism and only rely on the user for final approval. We then replace the traditional target-specific and inflexible mapping heuristics with a machine-learning based prediction mechanism, resulting in better mapping decisions while automating adaptation to different target architectures. We have evaluated our parallelization strategy on the NAS and SPEC CPU2000 benchmarks and two different multi-core platforms (dual quad-core Intel Xeon SMP and dual-socket QS20 Cell blade). We demonstrate that our approach not only yields significant improvements when compared with state-of-the-art parallelizing compilers, but comes close to and sometimes exceeds the performance of manually parallelized codes. On average, our methodology achieves 96% of the performance of the hand-tuned OpenMP NAS and SPEC parallel benchmarks on the Intel Xeon platform and gains a significant speedup for the IBM Cell platform, demonstrating the potential of profile-guided and machine-learning based parallelization for complex multi-core platforms.