Selecting Optimal Devices for Building an AI Compute Cluster

AI computing clusters play a crucial role in today's distributed environments, and choosing the right network communication equipment is essential for their performance and efficiency. In current AI computing clusters, data transmission consumes 80% of the power, with only a small percentage dedicated to actual computations. Additionally, 90% of the time is spent on disk I/O and network communication. Therefore, building an efficient AI compute cluster necessitates careful consideration when selecting appropriate connectivity devices to ensure seamless data flow and stable system operations.

AI compute clusters consist of a series of tightly interconnected computing nodes that collectively execute tasks to augment processing power. These clusters are capable of handling voluminous datasets, performing complex algorithms, and accelerating task completion through parallel computing. Network connectivity plays a vital role within the cluster's architecture, linking computation nodes and supporting data and resource sharing.

The selection of connectivity devices plays a crucial role in the overall performance and efficiency of AI clusters. These devices serve as the backbone of the cluster, facilitating data transfer, communication, and synchronization between nodes. The right choice of connectivity devices can significantly impact the cluster's throughput, latency, and overall reliability.

Several considerations come into play when selecting network communication equipment for AI computing clusters. These include packet loss rates, communication latency between computing nodes, and mechanisms to address node congestion. In a distributed environment, AI computing is limited by its weakest link. Frequent occurrences of the aforementioned factors can significantly impact overall computational performance.

Within the servers, there are two primary options for communication equipment: NVLink and PCIe. When it comes to communication between servers, three options exist:

NVSwitch, a product offered by NVIDIA, is typically bundled with their hardware devices and not sold separately. A few years ago, when deploying the Laxcus distributed operating system on DGX servers, NVSwitch was tested using network benchmarking tools. The results indicated that its communication efficiency was approximately twice that of an IB network. You can also read the post-An Overview of NVIDIA NVLink to find more information about NVSwitch.

InfiniBand (IB) networks have been the preferred solution for high-speed communication since their introduction in 2000. They offer advantages such as high speed, low latency, low packet loss rates, and remote direct memory access. IB networks are widely used in server clusters and supercomputers for high-performance computing scenarios. However, IB networks also have drawbacks, including high costs and challenges in maintenance, management, and scalability. While IB networks excel in small AI computing clusters, scalability becomes a challenge in large-scale AI computing clusters.

On the other hand, RoCE networks generally exhibit slightly lower communication efficiency compared to IB networks. The specific differences depend on various factors, including network configurations, workload characteristics, and application requirements. However, overall, RoCE networks have lower communication efficiency due to additional overhead caused by their protocol stack design and packet processing methods. In contrast, IB networks are designed specifically for high-performance computing and data center applications, offering lower latency and higher throughput.

Despite lower communication efficiency, RoCE networks have advantages in terms of cost and flexibility. Leveraging Ethernet as the underlying transport medium, RoCE networks can be deployed on existing Ethernet infrastructures, reducing costs and deployment complexity. Therefore, in certain scenarios, such as AI computing clusters or situations emphasizing flexibility and cost-effectiveness, RoCE networks remain a popular choice.

For more details, you can check the post-InfiniBand vs. RoCE: How to choose a network for Al data center?

FS specializes in crafting impeccable networking systems utilizing InfiniBand and RoCE technologies for the establishment of seamless network conditions and advanced computing powers. Tailoring to varied operational contexts and customer needs, FS meticulously selects the most fitting approach, enabling access to expansive data bandwidth, minimal delay in data transmission, and superior performance. By doing so, FS adeptly resolves hurdles associated with network congestion, thereby boosting overall network efficacy and elevating the end-user experience.

You can click on the H100 InfiniBand and 400G RoCE network solution pages for more details, or contact our solution experts to upgrade your network needs.

Understanding and evaluating needs, performance, cost, and security is crucial when choosing connectivity devices for an AI compute cluster. Ethernet switches and InfiniBand switches each have their advantages and considerations. By continuously assessing and updating devices, one can ensure the performance and stability of the AI compute cluster and promote successful AI project implementation. Selecting the appropriate connectivity devices allows the cluster to operate at maximum computational capacity, giving businesses a competitive edge.