A Brief on HPC / AI Networking Protocols - iWARP, TTPoE, Ultra Ethernet

1. Internet Wide Area RDMA Protocol (iWARP)

Overview:

iWARP implements RDMA over TCP/IP, making it suitable for existing wide-area Ethernet or IP networks without requiring a specialized fabric. It provides RDMA benefits like low latency and zero-copy transfers but at a slightly higher latency compared to RoCE or InfiniBand.

Use Cases:

1. Cloud-Distributed Storage: Enables high-performance data transfers over standard TCP/IP networks. Example: Distributed file systems like Ceph or Hadoop.
2. Remote Database Access: Low-latency access to remote databases in wide-area networks. Example: SQL or NoSQL databases in hybrid cloud environments.
3. Enterprise WANs: Connects data centers or edge locations with RDMA-enabled workloads over standard WAN links. Example: Long-distance data replication between data centers.
4. Compatibility with Existing Infrastructure: Useful for organizations with legacy TCP/IP networks that want to leverage RDMA benefits without major infrastructure changes.

2. Tesla Transport Protocol over Ethernet (TTPoE)

Overview:

Tesla Transport Protocol over Ethernet (TTPoE) is a networking protocol designed by Tesla for inter-GPU communication across Ethernet networks. It is optimized for high-performance AI workloads and enables low-latency communication between GPUs within and across servers.

Use Cases:

1. AI Training Clusters: Enables high-performance, low-latency communication between NVIDIA GPUs across Ethernet-based AI clusters. Example: Tesla's Dojo AI supercomputer uses TTPoE for inter-GPU communication.
2. AI Inference at Scale: Efficiently handles inference workloads by distributing tasks among GPUs. Example: Self-driving car AI systems using Dojo training clusters.
3. High-Throughput GPU Communication: Accelerates GPU workloads in Ethernet-based environments. Example: Multi-GPU setups in enterprise AI data centers.

3. Ultra Ethernet AI Protocol

Overview:

The Ultra Ethernet AI Protocol is a cutting-edge protocol designed specifically for AI workloads over Ethernet networks. It provides ultra-low latency and high throughput tailored to AI and machine learning needs, leveraging Ethernet's ubiquity.

Use Cases:

1. Scalable AI Workloads: Supports massive AI training and inference workloads across Ethernet-based networks. Example: Large language model (LLM) training such as GPT models on Ethernet fabrics.
2. Distributed Training: Enables efficient communication between GPUs and nodes in distributed AI training setups. Example: AI research labs running distributed reinforcement learning.
3. AI-Optimized Data Centers: Offers an Ethernet-based solution for next-generation AI data centers, reducing reliance on proprietary fabrics like InfiniBand. Example: Hyperscale cloud providers designing Ethernet-centric AI clusters.
4. Hybrid AI Workflows: Seamlessly integrates Ethernet for mixed AI and non-AI workloads in shared data center environments. Example: Cloud providers offering hybrid compute and AI services.

Note: Please refer my previous posts for RoCEv2 and InfiniBand Protocols