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arXiv · 2202.00717

Pipeflow: An Efficient Task-Parallel Pipeline Programming Framework using Modern C++

Cheng-Hsiang Chiu·Tsung-Wei Huang·Zizheng Guo·Yibo Lin
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Abstract

Pipeline is a fundamental parallel programming pattern. Mainstream pipeline programming frameworks count on data abstractions to perform pipeline scheduling. This design is convenient for data-centric pipeline applications but inefficient for algorithms that only exploit task parallelism in pipeline. As a result, we introduce a new task-parallel pipeline programming framework called Pipeflow. Pipeflow does not design yet another data abstraction but focuses on the pipeline scheduling itself, enabling more efficient implementation of task-parallel pipeline algorithms than existing frameworks. We have evaluated Pipeflow on both micro-benchmarks and real-world applications. As an example, Pipeflow outperforms oneTBB 24% and 10% faster in a VLSI placement and a timing analysis workloads that adopt pipeline parallelism to speed up runtimes, respectively.

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Cheng-Hsiang Chiu, Tsung-Wei Huang, Zizheng Guo, Yibo Lin. 2022-02-01. Pipeflow: An Efficient Task-Parallel Pipeline Programming Framework using Modern C++. https://arxiv.org/abs/2202.00717

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