提出双策略框架，优化异步数据流中任务分配，提升设备利用率和执行效率。
DOPPLER: Dual-Policy Learning for Device Assignment in Asynchronous Dataflow Graphs
- 设计选择与放置双重策略网络，协同决策操作分配。
- 在多任务上降低系统执行时间，训练效率提升显著。
- 适合复杂机器学习负载的异步系统调度，兼顾性能与效率。
我们研究在工作就绪系统中，将数据流图中的操作分配到设备以最小化执行时间的问题，重点针对复杂的机器学习工作负载。现有基于学习的方法常因三大局限而表现不佳：（1）依赖如TensorFlow等批量同步系统，因屏障同步导致设备利用率低下；（2）设计学习方法时忽视底层系统的调度机制；（3）过度依赖强化学习，忽略专家设计的有效启发式规则。本文提出Doppler，一种三阶段训练双策略网络的框架，包含（1）$—{SEL}$策略用于选择操作，（2）$—{PLC}$策略用于将选定操作放置到设备。实验表明，Doppler在所有任务上均优于基线方法，有效降低系统执行时间，并展现出采样效率优势，显著减少每轮训练时间。
原文摘要 · Abstract (English)
We study the problem of assigning operations in a dataflow graph to devices to minimize execution time in a work-conserving system, with emphasis on complex machine learning workloads. Prior learning-based methods often struggle due to three key limitations: (1) reliance on bulk-synchronous systems like TensorFlow, which under-utilize devices due to barrier synchronization; (2) lack of awareness of the scheduling mechanism of underlying systems when designing learning-based methods; and (3) exclusive dependence on reinforcement learning, ignoring the structure of effective heuristics designed by experts. In this paper, we propose Doppler, a three-stage framework for training dual-policy networks consisting of 1) a $\mathsf{SEL}$ policy for selecting operations and 2) a $\mathsf{PLC}$ policy for placing chosen operations on devices. Our experiments show that Doppler outperforms all baseline methods across tasks by reducing system execution time and additionally demonstrates sampling efficiency by reducing per-episode training time.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。