With the rapid development of artificial intelligence (AI) technology, AI computing networks, as a key infrastructure supporting the efficient operation of AI applications, are becoming increasingly important. In the construction of AI computing networks, the choice of network architecture has a decisive impact on system performance, cost, and scalability. Currently, there are two main architectures in the market: InfiniBand and RoCEv2. This article will explore these two architectures in depth and analyze their differences.
I. InfiniBand Network Architecture
InfiniBand is a high-performance, low-latency network communication technology designed specifically for massively parallel computing systems. It employs a unique communication protocol that enables high-speed data transmission and efficient resource scheduling. Key components of the InfiniBand network architecture include the Subnet Manager (SM), InfiniBand network interface cards (NICs), InfiniBand switches, and InfiniBand cables.
In an InfiniBand network, the Subnet Manager (SM) plays a central role. It is responsible for the centralized management of the entire network, including device configuration, routing information maintenance, and network resource scheduling. Through the Subnet Manager, the InfiniBand network can achieve efficient resource allocation and load balancing, thereby ensuring stable system operation.
Furthermore, the InfiniBand network employs link-level flow control and adaptive routing technology. Link-level flow control prevents buffer overflows or packet loss caused by excessive data transmission, ensuring the continuity and stability of data transmission. Adaptive routing technology dynamically selects routes based on the specific circumstances of each data packet, achieving real-time optimization of network resources and optimal load balancing.
II. RoCEv2 Network Architecture
RoCEv2 (RDMA over Converged Ethernet version 2) is a Remote Direct Memory Access (RDMA) technology based on Ethernet, designed to provide high-performance, low-latency network communication. The RoCEv2 network architecture uses an Ethernet network layer and a UDP transport layer, replacing the InfiniBand network layer, thus providing better scalability.
In RoCEv2 networks, RDMA technology is key to achieving efficient data transmission. RDMA allows one host to directly access the memory of another host without intervention from the operating system kernel. This direct access bypasses the traditional TCP/IP protocol stack, reducing data transmission latency and overhead, and improving overall system performance.
Furthermore, RoCEv2 networks offer excellent versatility and low cost. Because it uses Ethernet as its underlying transmission technology, it is seamlessly compatible with existing Ethernet infrastructure, reducing system construction and maintenance costs. Additionally, RoCEv2 networks support multiple operating systems and hardware platforms, demonstrating good scalability and compatibility.
III. Difference Analysis between InfiniBand and RoCEv2
From a performance perspective, InfiniBand networks demonstrate significant advantages in application-layer service performance, especially in large-scale scenarios, where they deliver superior network throughput. RoCEv2 networks, on the other hand, are favored for their strong versatility and lower cost, being suitable not only for building high-performance RDMA networks but also for seamless compatibility with existing Ethernet infrastructure.
From a cost perspective, RoCEv2 networks have an advantage due to their good versatility and lower construction costs. In contrast, InfiniBand networks require additional hardware support and have higher construction costs.
In summary, InfiniBand and RoCEv2, as the two mainstream architectures for AI intelligent computing networks, each have unique advantages and applicable scenarios. When choosing a network architecture, it is necessary to comprehensively consider factors such as specific business requirements, system scale, and cost budget.
