CPP-taskflow: Fast task-based parallel programming using modern C++

Tsung Wei Huang, Chun Xun Lin, Guannan Guo, Martin Wong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper we introduce Cpp-Taskflow, a new C++ tasking library to help developers quickly write parallel programs using task dependency graphs. Cpp-Taskflow leverages the power of modern C++ and task-based approaches to enable efficient implementations of parallel decomposition strategies. Our programming model can quickly handle not only traditional loop-level parallelism, but also irregular patterns such as graph algorithms, incremental flows, and dynamic data structures. Compared with existing libraries, Cpp-Taskflow is more cost efficient in performance scaling and software integration. We have evaluated Cpp-Taskflow on both micro-benchmarks and real-world applications with million-scale tasking. In a machine learning example, Cpp-Taskflow achieved 1.5-2.7× less coding complexity and 14-38% speed-up over two industrial-strength libraries OpenMP Tasking and Intel Threading Building Blocks (TBB).

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium, IPDPS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages974-983
Number of pages10
ISBN (Electronic)9781728112466
DOIs
StatePublished - May 2019
Event33rd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2019 - Rio de Janeiro, Brazil
Duration: May 20 2019May 24 2019

Publication series

NameProceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium, IPDPS 2019

Conference

Conference33rd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2019
CountryBrazil
CityRio de Janeiro
Period5/20/195/24/19

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems and Management

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