793 B
793 B
- 方法A、方法B都用于解决同一个问题，融合A+B方法， 代表论文：A Unified Architecture for Accelerating Distributed DNN Training in Heterogeneous GPU/CPU Clusters; Hydra: Deadline-Aware and Efficiency-Oriented Scheduling for Deep Learning Jobs on Heterogeneous GPUs 说明A、B方法各自的优点与缺点，再提出如何将A+B结合，实现更好的效果。
- 方法A直接用在B领域会存在问题， 代表论文：MultiRAG: A Knowledge-Guided Framework for Mitigating Hallucination in Multi-Source Retrieval Augmented Generation; TrajMesa: A Distributed NoSQL-Based Trajectory Data Management System NALSpatial: A Natural Language Interface for Spatial Databases
- 论文A考虑了A因素，论文b考虑了B因素，联合考虑A+B， 代表论文：