Taskflow: A General-Purpose Parallel and Heterogeneous Task Programming System

Taskflow tackles the long-standing question: How can we make it easier for developers to program parallel and heterogeneous computer-aided design (CAD) applications with high performance and simultaneous high productivity? Taskflow introduces a new powerful task graph programming model to assist dev...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems Vol. 41; no. 5; pp. 1448 - 1452
Main Authors: Huang, Tsung-Wei, Lin, Dian-Lun, Lin, Yibo, Lin, Chun-Xun
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
ISSN:0278-0070, 1937-4151
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Taskflow tackles the long-standing question: How can we make it easier for developers to program parallel and heterogeneous computer-aided design (CAD) applications with high performance and simultaneous high productivity? Taskflow introduces a new powerful task graph programming model to assist developers in the implementation of parallel and heterogeneous algorithms with complex control flow. We develop an efficient system runtime to solve many of the new scheduling challenges arising out of our models and optimize the performance across latency, energy efficiency, and throughput. Taskflow has demonstrated promising performance on both micro-benchmarks and real-world applications. As an example, Taskflow solved a large-scale circuit placement problem up to 17% faster, with <inline-formula> <tex-math notation="LaTeX">1.3\times </tex-math></inline-formula> fewer memory, <inline-formula> <tex-math notation="LaTeX">2.1\times </tex-math></inline-formula> less power consumption, and <inline-formula> <tex-math notation="LaTeX">2.9\times </tex-math></inline-formula> higher throughput than two industrial-strength systems, oneTBB and StarPU, on a machine of 40 CPUs and 4 GPUs.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2021.3082507