Was this Blogpost written by AI? 

AI has made significant progress in recent years and is influencing our lives. For example, we can ask AI questions and receive answers in text and speech almost instantly. 
AI can even generate realistic sound, image, and video material from a simple description.

To make all this possible, we need a very fast network infrastructure, which ultimately benefits the end user. This network infrastructure is located in our data centers and operates with the InfiniBand network protocol. To make InfiniBand usable for Ethernet, you need a protocol conversion.

InfiniBand - the high-speed network technology is the key to efficient processing of AI tasks.

Structure

This gives a general idea of how the network infrastructure is structured, but real-world setups can look quite different.
The example is based on the NVIDIA portfolio for AI applications.

Definition and fields of application

InfiniBand is a network protocol specifically developed for high speeds and low latency. It is a "must-have" for transferring large amounts of data quickly and reliably.

Therefore, it makes sense to use this protocol for AI applications that require high computational power and fast data transfer.

InfiniBand is primarily used in data centers, where the technology is employed in high-performance computers. In addition to AI, InfiniBand devices are also used in areas such as cloud computing and financial services.

Important abbreviations

• InfiniBand: The high-performance networking protocol InfiniBand, known for its high data rates and low latency, is a prerequisite for an AI node.
• HPC Cluster: Stands for High-Performance Computing Cluster, used for compute-intensive workloads such as AI, simulation, or scientific computing.
• RoCEv2 (RDMA over Converged Ethernet version 2): Enables RDMA over Ethernet. It's required when an HPC cluster needs to be connected directly to an Ethernet leaf switch.
• RDMA (Remote Direct Memory Access): A technology that enables direct memory access between two devices without burdening the processor.
• InfiniBand HDR (High Data Rate – 200 Gbps), EDR (Enhanced Data Rate – 100 Gbps), and NDR (Next Data Rate – 400 Gbps): Terms for the different speed levels of InfiniBand.
• Mellanox NVIDIA Networking: Currently the market leader in InfiniBand hardware, providing switches, adapters, and software solutions optimized for low-latency, high-throughput networks.
• Low Latency: A key term describing the low delay in data transmission.
  
  

An Infiniband network consists of

• HPC High-Performance Computers: Equipped with GPUs and TPUs optimized for machine learning and AI.
• InfiniBand Network Interface Cards (NICs) / Host Channel Adapter (HCA): (e.g. NVIDIA ConnectX-Series) NICs connect high-performance computers to the network, offering extremely low latency and high throughput rates.
• InfiniBand Switches: Switches route data packets between network devices.
• InfiniBand Cables and Optical Modules: Physical connections between network devices.
• Subnet Manager: (Software component on an InfiniBand switch) identifies InfiniBand devices, manages, prioritizes data rate/bandwidth of devices, and configures them.
  
  

Advantages and Disadvantages

Conclusion

In NVIDIA-based data center environments, InfiniBand has emerged as a leading network technology for AI workloads, due to increasing data traffic and the need for high performance and reliability. As technology and expertise continue to evolve, many of the current challenges are expected to be addressed. In this specific context, the benefits of using InfiniBand tend to outweigh its drawbacks.

Yes, parts of this blog post were written with the help of AI – and if you're curious, you could always ask AI yourself :)