.png){kind=icon}
返回
Taskflow: A Lightweight Parallel and Heterogeneous Task Graph Computing System
.png){kind=icon}
OA
AI
DOI：10.1109/TPDS.2021.3104255.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Taskflow aims to streamline the building of parallel and heterogeneous applications using a lightweight task graph-based approach. Taskflow introduces an expressive task graph programming model to assist developers in the implementation of parallel and heterogeneous decomposition strategies on a heterogeneous computing platform. Our programming model distinguishes itself as a very general class of task graph parallelism with in-graph control flow to enable end-to-end parallel optimization. To support our model with high performance, we design an efficient system runtime that solves many of the new scheduling challenges arising out of our models and optimizes the performance across latency, energy efficiency, and throughput. We have demonstrated the promising performance of Taskflow in real-world applications. As an example, Taskflow solves a large-scale machine learning workload up to 29% faster, 1.5x less memory, and 1.9x higher throughput than the industrial system, oneTBB, on a machine of 40 CPUs and 4 GPUs. We have opened the source of Taskflow and deployed it to large numbers of users in the open-source community.
Keyword:
Task analysis
Parallel processing
Graphics processing units
Computational modeling
Programming
Solid modeling
Runtime
Parallel programming
task parallelism
high-performance computing
modern C plus plus programming
期刊
IF:
6
论文数:
5.2K
被引数:
1.1W
机构
引用论文
Peripherally administered ghrelin induces bimodal effects on the mesolimbic dopamine system depending on food-consumptive states.png)
Neuroscience
NeuroscienceIF0
PREAI
分享收藏
Genes, circuits, and precision therapies for autism and related neurodevelopmental disorders.
IF0
PREAI
分享收藏
查看更多内容