A Pipeline Pattern Detection Technique in Polly

Talaashrafi, Delaram; Doerfert, Johannes; Maza, Marc Moreno

doi:10.1145/3547276.3548445

Cited by 1 publication

(2 citation statements)

References 26 publications

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“…two categories: static and dynamic. Static tools 8,10,[36][37][38] have a narrow scope for program analysis owing to the limitations of static analysis. 39 They do not capture the data dependencies that actually occur at runtime.…”

Section: Related Workmentioning

confidence: 99%

“…The program analysis tools that have been developed to help programmers discover parallelisms in sequential programs can be divided into two categories: static and dynamic. Static tools 8,10,36‐38 have a narrow scope for program analysis owing to the limitations of static analysis 39 . They do not capture the data dependencies that actually occur at runtime.…”

Section: Related Workmentioning

confidence: 99%

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Multigraph learning for parallelism discovery in sequential programs

Shen

Peng

et al. 2023

Concurrency and Computation

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Summary Parallelization is an optimization technique that is playing an increasingly vital role in software applications. Discovering the potential parallelism in sequential programs is of primary importance for parallelization. To this end, various tools have been created to obtain parallelizable targets by analyzing the dependencies in sequential programs. However, the majority are constructed based on manually designed dependence analysis rules. Consequently, they must follow some constraints, which limit the scope of their program analysis capability. Furthermore, constructing a dependency analysis rule is a complicated and highly sophisticated endeavor. In this study, we exploit the fact that neural network models can learn the potential features of programs to tackle the parallelism discovery task in an end‐to‐end manner. Specifically, we developed a multigraph learning architecture on top of multiple abstract code representations in a complementary manner. In the architecture, we equip code representations with different neural network models in a targeted manner, including a deep convolutional neural network for analyzing both control and data flows and a novel neural network for learning the abstract syntax trees. Experimental results obtained on a common parallelism discovery dataset indicate that the developed multigraph learning architecture can learn potential parallelism patterns in sequential programs with high accuracy and efficiency.

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