论文标题
Celeritas：大数据流图的快速优化器
Celeritas: Fast Optimizer for Large Dataflow Graphs
论文作者
论文摘要
快速扩大的神经网络模型在单个设备上运行越来越具有挑战性。因此，多个设备上的模型并行性对于确保训练大型模型的效率至关重要。最近的建议在长时间处理时间或性能差。因此，我们提出了Celeritas，这是一个快速的框架，用于优化大型型号的设备放置。 Celeritas在标准评估中采用简单但有效的模型并行化策略，并通过一系列调度算法生成位置策略。我们进行实验以在许多大型模型上部署和评估Celeritas。结果表明，与大多数高级方法相比，Celeritas不仅将放置策略的生成时间减少了26.4 \％，而且还将模型运行时间提高了34.2 \％。
The rapidly enlarging neural network models are becoming increasingly challenging to run on a single device. Hence model parallelism over multiple devices is critical to guarantee the efficiency of training large models. Recent proposals fall short either in long processing time or poor performance. Therefore, we propose Celeritas, a fast framework for optimizing device placement for large models. Celeritas employs a simple but efficient model parallelization strategy in the Standard Evaluation, and generates placement policies through a series of scheduling algorithms. We conduct experiments to deploy and evaluate Celeritas on numerous large models. The results show that Celeritas not only reduces the placement policy generation time by 26.4\% but also improves the model running time by 34.2\% compared to most advanced methods.
