Perspective: A Sensible Approach to Speculative Automatic Parallelization [abstract] (ACM DL, PDF)
Sotiris Apostolakis, Ziyang Xu, Greg Chan, Simone Campanoni, and David I. August
Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), March 2020.
Accept Rate: 18% (86/476).
Awarded all top ACM Reproducibility Badges offered by the Artifact Evaluation Committee.

The promise of automatic parallelization, freeing programmers from the error-prone and time-consuming process of making efficient use of parallel processing resources, remains unrealized. For decades, the imprecision of memory analysis limited the applicability of non-speculative automatic parallelization. The introduction of speculative automatic parallelization overcame these applicability limitations, but, even in the case of no misspeculation, these speculative techniques exhibit high communication and bookkeeping costs for validation and commit. This paper presents Perspective, a speculative-DOALL parallelization framework that maintains the applicability of speculative techniques while approaching the efficiency of non-speculative ones. Unlike current approaches which subsequently apply speculative techniques to overcome the imprecision of memory analysis, Perspective combines a novel speculation-aware memory analyzer, new efficient speculative privatization methods, and a planning phase to select a minimal-cost set of parallelization-enabling transforms. By reducing speculative parallelization overheads in ways not possible with prior parallelization systems, Perspective obtains higher overall program speedup (23.0x for 12 general-purpose C/C++ programs running on a 28-core shared-memory commodity machine) than Privateer (11.5x), the prior automatic DOALL parallelization system with the highest applicability.