CPU和CPU，是如何通信的？

Core Viewpoint - The article discusses the advancements in GPU communication technologies, particularly focusing on GPUDirect Storage, GPUDirect P2P, NVLink, NVSwitch, and GPUDirect RDMA, which enhance data transfer efficiency and reduce bottlenecks in high-performance computing environments [27]. Group 1: GPU and Storage Communication - The data flow from storage systems to GPU memory involves two data copies: from NVMe SSD to system memory and then from system memory to GPU memory, which introduces redundancy [6]. - GPUDirect Storage allows direct access from storage to GPU memory, significantly improving data loading efficiency by reducing unnecessary system copies [7]. Group 2: GPU to GPU Communication - Traditional GPU communication involves multiple data copies through system memory, which can be inefficient [10]. - GPUDirect P2P enables direct data transfer between GPUs, bypassing the CPU and reducing data copy actions by half [12]. Group 3: NVLink and NVSwitch - NVLink provides high bandwidth for data transfer between GPUs, achieving up to 600GB/s for NVIDIA A100 Tensor Core GPUs, which is significantly higher than traditional PCIe [15]. - NVSwitch facilitates full interconnectivity among multiple GPUs, supporting high bandwidth and scalability for large GPU systems [20]. Group 4: Cross-Machine Communication - Traditional cross-machine communication requires multiple steps involving system memory, which can be inefficient [22][24]. - GPUDirect RDMA simplifies this process, allowing direct access to GPU memory from peripheral PCIe devices, thus enhancing communication efficiency [25]. Group 5: Summary of Technologies - The combination of GPUDirect technologies, including P2P and RDMA, supports efficient communication within single nodes and across multiple nodes, essential for AI training and high-performance computing [28].