📄 中文摘要
在多GPU环境中，CPU与GPU之间的交互是通过主机-设备范式实现的。该范式定义了计算任务的分配与执行方式，CPU作为主机负责管理和调度，而GPU作为设备则专注于高效的并行计算。通过合理配置和优化数据传输，能够显著提升深度学习模型的训练速度与性能。理解这一交互机制对于开发高效的AI应用至关重要，尤其是在处理大规模数据集时。掌握主机与设备之间的协作关系，有助于研究人员和开发者更好地利用多GPU系统的潜力。
多GPU中的人工智能：理解主机与设备的范式
出处: AI in Multiple GPUs: Understanding the Host and Device Paradigm
发布: 2026年2月12日
📄 English Summary
AI in Multiple GPUs: Understanding the Host and Device Paradigm
In a multi-GPU environment, the interaction between CPU and GPUs is facilitated through the host-device paradigm. This paradigm defines how computational tasks are allocated and executed, with the CPU acting as the host responsible for management and scheduling, while the GPU serves as the device focused on efficient parallel computation. By optimizing data transfer and configuration, significant improvements in the training speed and performance of deep learning models can be achieved. Understanding this interaction mechanism is crucial for developing efficient AI applications, especially when dealing with large-scale datasets. Mastering the collaboration between host and device helps researchers and developers better leverage the potential of multi-GPU systems.