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The High-Performance Network Revolution in the AI Era: RDMA
< BackThe High-Performance Network Revolution in the AI Era: RDMA
We are currently in a golden age of development driven by artificial intelligence and big data. From ChatGPT to autonomous driving, from gene sequencing to financial risk management, the demand for computing power is growing exponentially across industries. However, when we focus on the internal operations of computing clusters, we discover an often-overlooked bottleneck: network transmission efficiency.
During data communication, traditional TCP/IP protocol stacks require multiple data copies. Data is copied from the network card to the kernel buffer and then from the kernel buffer to user space. The CPU must participate in every step of protocol processing. This approach not only increases latency but also consumes a large amount of valuable CPU computing resources. When hundreds or even thousands of servers in an AI training cluster need to exchange gradient data frequently, the limitations of traditional networks become obvious: high network latency, high CPU utilization, and low bandwidth utilization.
Against this backdrop, RDMA (Remote Direct Memory Access) technology has expanded from the high-performance computing field into a much broader range of applications, becoming a key technology for building modern AI infrastructure.
RDMA Technology Explained: Redefining the Network Communication Paradigm
What Is RDMA?
RDMA is a networking technology that enables direct access to the memory of a remote host. Unlike traditional network communication, RDMA can transfer data directly from the memory of one host to the memory of another without involving the CPU or operating system. This point-to-point memory connection fundamentally changes the data transmission paradigm within data centers.
Three Core Advantages of RDMA
(1) Zero-Copy
In traditional network communication, data must be copied multiple times between user space, kernel buffers, and network buffers. RDMA bypasses the operating system, allowing data to be transferred directly from the sender's application memory to the receiver's application memory, eliminating unnecessary data-copying overhead. Zero-copy technology can improve data transmission efficiency several times over.
(2) Kernel Bypass
RDMA allows applications to interact directly with network card hardware, completely bypassing the operating system kernel. This means network communication no longer requires complex protocol stack processing. The data path is simplified from “Application → Kernel → NIC” to “Application → NIC.” For high-performance computing and AI training scenarios, this can reduce latency from milliseconds to microseconds.
(3) CPU Offload
Since protocol processing and data transmission are handled entirely by network card hardware, the CPU is freed from intensive networking tasks. In a typical AI training task, CPU resources consumed by gradient synchronization can be reduced by more than 70%. These resources can be redirected toward model training, significantly improving overall training efficiency.
The RDMA Protocol Debate: RoCEv2 vs. iWARP
Currently, the two mainstream RDMA implementation solutions are RoCEv2 and iWARP. Although both belong to RDMA technology, they differ in protocol stack design, hardware requirements, and application scenarios.
RoCEv2 (RDMA over Converged Ethernet v2)
RoCEv2 is an RDMA implementation based on UDP. It requires a lossless network environment, typically relying on DCB/PFC technologies. Its advantages include:
• Lower latency: A more streamlined protocol stack enables microsecond-level latency.
• Higher bandwidth utilization: Particularly suitable for high-traffic and high-bandwidth scenarios.
• Strong compatibility: Offers good compatibility with standard Ethernet switches.
Typical applications: High-performance computing clusters, AI training clusters, and distributed storage networks within data centers.
iWARP (Internet Wide Area RDMA Protocol)
iWARP is an RDMA implementation based on TCP, with relatively flexible network environment requirements. Its advantages include:
• Better compatibility: Supports standard Ethernet switches and routers without requiring a lossless network.
• Longer transmission distances: Can operate across wide area networks, making it suitable for interconnecting geographically distributed data centers.
• Higher reliability: Leverages TCP congestion control and retransmission mechanisms.
Typical applications: Interconnection between geographically distributed data centers, complex and changing network environments, and business applications requiring reliable TCP transmission.
Users can choose the appropriate solution based on their actual network environment and business requirements, balancing performance with deployment flexibility.
RDMA Application Scenarios: Beyond High-Performance Computing
AI Training Clusters
In deep learning training, gradient synchronization is a key factor limiting training efficiency. In a cluster with thousands of GPUs, every parameter update requires massive amounts of gradient data to be synchronized across nodes. With RDMA networking, gradient synchronization time can be reduced by more than 80%, significantly improving daily training efficiency. This is one reason why technology giants such as OpenAI and Google have adopted RDMA networks as a standard component of AI infrastructure.
High-Performance Computing (HPC)
In HPC scenarios such as weather forecasting, gene sequencing, and molecular dynamics simulations, the volume of data exchanged between nodes is enormous. RDMA can reduce communication latency between computing nodes to microsecond levels, optimizing the overlap between computation and communication and significantly shortening the cycle of scientific discovery.
Distributed Storage (Ceph, MinIO, Spark)
Distributed storage systems are highly sensitive to network latency. In Ceph distributed storage, for example, data synchronization between OSDs, heartbeat detection, and client I/O requests all rely on efficient network communication. RDMA can reduce single-operation I/O latency by more than 60%, significantly improving the overall throughput and response speed of storage clusters.
Financial High-Frequency Trading
In financial trading, where milliseconds or even microseconds can determine the outcome, network latency directly affects the execution of trading strategies. RDMA technology can enable sub-microsecond order routing and market data distribution, helping quantitative trading teams gain an advantage in highly competitive markets.
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