Year: 2024
Type: conference-paper
Abstract: The pipeline is a fundamental pattern to parallelize a series of stage tasks over a sequence of data in loops. Mainstream pipeline programming frameworks count on data abstractions to perform pipeline scheduling. Although this design is convenient for data-centric parallel applications, it is not efficient for algorithms that only exploit task parallelism in the pipeline. To address the limitation, we introduce a new task-parallel pipeline programming framework called Pipeflow. Pipeflow separates data abstractions and task scheduling, enabling a more efficient implementation of task-parallel pipeline algorithms than existing frameworks. We have evaluated Pipeflow on both micro-benchmarks and real-world applications. For example, in a timing analysis workload that explores pipeline parallelism to speed up the runtime performance, the Pipeflow’s implementation outperforms the oneTBB’s implementation up to 110.33% faster.
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Awards: OAC Core: Transpass: Transpiling Parallel Task Graph Programming Models for Scientific Software, CAREER: HeteroTime: Accelerating Static Timing Analysis with Intelligent Heterogeneous Parallelism, SHF: Small: A General-purpose Parallel and Heterogeneous Task Graph Computing System for VLSI CAD, POSE: Phase I: Toward a Task-Parallel Programming Ecosystem for Modern Scientific Computing, G6671297155, G907103759
OpenAlex ID: W4391053240