动态分配任务，让不同速度的设备高效协同训练。
ATA: Adaptive Task Allocation for Efficient Resource Management in Distributed Machine Learning
- 根据设备实时性能动态调整任务分配，避免资源浪费。
- 实验显示比传统方法节省大量计算成本，且无上限增长风险。
- 适合异构设备集群，尤其在计算速度差异大的场景下优势明显。
异步方法是分布式机器学习中并行计算的核心，旨在通过充分利用所有可用资源来加速训练。然而，其贪婪的任务分配策略可能导致资源过度使用，尤其在设备间计算时间不一致时更为显著。若事先知晓各设备的计算时间分布，便可将更多任务分配给更快的设备，实现高效训练。问题在于如何在缺乏先验知识的情况下达到最优分配。本文提出ATA（自适应任务分配）机制，能够适应异构且随机的设备计算时间分布。理论分析表明，ATA可实现最优任务分配，性能接近已知计算时间分布的方法。实验进一步验证，ATA在资源利用上表现优异，相比贪婪方法显著降低开销，且开销不会随设备数量无限制增长。
原文摘要 · Abstract (English)
Asynchronous methods are fundamental for parallelizing computations in distributed machine learning. They aim to accelerate training by fully utilizing all available resources. However, their greedy approach can lead to inefficiencies using more computation than required, especially when computation times vary across devices. If the computation times were known in advance, training could be fast and resource-efficient by assigning more tasks to faster workers. The challenge lies in achieving this optimal allocation without prior knowledge of the computation time distributions. In this paper, we propose ATA (Adaptive Task Allocation), a method that adapts to heterogeneous and random distributions of worker computation times. Through rigorous theoretical analysis, we show that ATA identifies the optimal task allocation and performs comparably to methods with prior knowledge of computation times. Experimental results further demonstrate that ATA is resource-efficient, significantly reducing costs compared to the greedy approach, which can be arbitrarily expensive depending on the number of workers.
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