Scalable RoCEv2 Deployments: Overcoming Network Challenges in AI Data Centers

RoCEv2 (RDMA over Converged Ethernet version 2) is a powerful technology that extends the capabilities of RDMA (Remote Direct Memory Access) by allowing it to function over both Layer 2 (L2) and Layer 3 (L3) networks, including environments with multiple subnets. This extension is particularly useful for elastic and scalable deployments, making it a popular choice for organizations looking to enhance the efficiency of their data centers.

However, deploying RoCEv2 at scale introduces potential challenges, particularly in terms of network configuration and performance optimization. Incorrect settings can significantly impact application performance, which makes it crucial to validate switch fabric performance and optimize configurations. Ensuring network stability and resiliency, especially under congestion, is vital.

Key congestion control mechanisms in RoCEv2 environments include Data Center Quantized Congestion Notification (DCQCN) and Priority Flow Control (PFC). These mechanisms are essential for managing network traffic and preventing congestion, ensuring that the network can handle the demands of large-scale deployments.

Traditional testing methods, such as using homegrown or open-source test solutions with real servers, may not be sufficient to address the complexity and scale of modern data centers. 

Spirent and the Collective Communication Library

Spirent offers a solution that stands out in the industry by emulating Collective Communication Library (CCL) traffic patterns using RoCEv2 over Ethernet. This solution allows for accurate measurement of key performance indicators (KPIs) for AI Ethernet fabric, enabling data centers to validate their Ethernet infrastructure's capability to support large AI workloads. Importantly, this approach avoids the challenges associated with deploying and managing xPU-based server farms as testing frameworks, offering a more scalable and efficient testing solution.

The Collective Communication Library (CCL) is critical in AI data centers due to the vast amounts of data communication required between xPUs (processing units). As AI workloads grow in complexity, the data processing needs to be distributed across multiple xPUs to manage the load effectively. CCL, which is built on open MPI (Message Passing Interface), facilitates high-performance computing (HPC) and parallel processing, essential for the efficient functioning of AI data centers.

Spirent's implementation of CCL supports the most common traffic patterns used in AI environments, such as AlltoAll, which is widely referenced in NVIDIA's NCCL (NVIDIA Collective Communication Library). Through user-friendly wizards, Spirent enables easy generation of these AI traffic patterns, which are crucial for the realistic testing of AI network infrastructures. When network congestion occurs, Spirent's solution leverages DCQCN (Data Center Quantized Congestion Notification) and PFC (Priority Flow Control) to mirror the behavior of actual AI networks, ensuring that the testing environment accurately reflects real-world conditions.

One of the key metrics used to evaluate network performance in these scenarios is the Job Completion Time (JCT), which is reported at the end of each test. JCT provides insights into how efficiently the network fabric handles AI workloads, particularly under conditions of high traffic and congestion.

Spirent's AI testing solution is designed to be high-density, multi-speed, cost-effective, and easy to deploy. It is built on an open and transparent architecture that ensures consistent and repeatable results. This solution generates realistic AI traffic patterns at line rate while supporting necessary congestion controls. Users can measure a variety of key performance indicators (KPIs), including:

• Job Completion Time (JCT)
• Throughput
• Tail Latency
• Packet Latency
• Packet Loss
• Re-ordered Packet Count
• Late Packet Count

These KPIs are vital for assessing the resiliency of the AI network fabric under congestion conditions and during link flap scenarios. This allows for thorough characterization and optimization of the AI fabric, including tuning network configurations such as buffer sizes, ECN (Explicit Congestion Notification), load-balancing algorithms, and Quality of Service (QoS) settings.

Spirent's AI test solution operates on top-tier, high-density, multi-speed Ethernet test modules, including the A1-400-QD-16 platform, which is recognized as the industry's highest-density 400GE platform. This platform supports the emulation of AI workloads and is versatile enough to be used for both AI-specific and general routing/switching tests. Other modules like the FX3-QSFP28-6, MX3-QSFP28-4, and FX3-QSFP28-4 provide flexibility in port configurations, making them ideal for a wide range of testing scenarios, including RoCEv2 testing.

Moreover, Spirent's solution supports multi-user environments, allowing per-port user reservation. This means ports from a single test module can be allocated to either single or multiple concurrent test sessions. This multi-user functionality, combined with the ability to conduct regular L2-3 tests at various speeds (400/200/100/50/40/25/10G), maximizes the return on investment for these testing platforms.

Finally, this AI testing solution is an integral component of the Spirent TestCenter and works seamlessly with other components to deliver easy, consistent scripting via TCL and REST API, along with Command Sequencer NoCode automation, enhancing the efficiency and reliability of test processes.

Why WWT for AI solution testing?

WWT is well-positioned to accelerate your AI journey — no matter where you are in terms of maturity. Our AI Proving Ground is a dynamic environment composed of industry-leading software, hardware and component solutions that can be integrated quickly. Combined with the knowledge and experience of our AI and infrastructure experts, and supported by our longstanding manufacturer partnerships, the AI Proving Ground allows organizations to experience the art of the possible for themselves while accelerating their time to market.

Developed within our Advanced Technology Center (ATC), this one-of-a-kind lab environment empowers IT teams to evaluate and test AI infrastructure, software and solutions for efficacy, scalability and flexibility — all under one roof. The AI Proving Ground provides visibility into data flows across the entire development pipeline, enabling more informed decision-making while safeguarding production environments.

Inside the AI Proving Ground, AI testing with RoCEv2 transforms the landscape of HPC and data center operations by enabling faster, more efficient, and cost-effective AI workload management. By leveraging RoCEv2's ability to offer low-latency and high-throughput communication, companies can scale their AI training environments seamlessly. This capability is crucial for supporting advanced AI and deep learning models that require large-scale data parallelism and significant computational resources distributed across multiple nodes and GPUs.

The reduction in CPU overhead further allows companies to maximize their existing hardware investments. By offloading network processing from the CPU to the NIC, more CPU resources become available for compute-intensive tasks, leading to better overall system performance and faster completion times for AI training and inference tasks. This not only optimizes resource utilization but also reduces the total cost of ownership (TCO), making it an economically viable solution compared to traditional networking models. Moreover, the use of standard Ethernet infrastructure with RoCEv2 eliminates the need for costly specialized networking hardware, providing a more cost-effective approach to achieving high performance and low latency in data center operations.

RoCEv2 benefits

For WWT clients, RoCEv2 offers clear benefits in terms of faster AI model training and inference times, which are crucial for businesses that rely on AI to drive their core operations, product development, and service delivery. With RoCEv2, clients can experience reduced time-to-market for AI-driven solutions, enabling them to stay ahead of competitors by rapidly deploying new features and capabilities. The low-latency environment facilitated by RoCEv2 is particularly beneficial for real-time AI applications, such as autonomous systems, fraud detection, and high-frequency trading, where milliseconds matter. By ensuring quick data transfer and processing speeds, RoCEv2 supports a more responsive and reliable AI application ecosystem.

The cost savings resulting from using RoCEv2 cannot be overstated. Clients benefit from optimized data center operations that reduce overhead costs, energy consumption, and infrastructure expenses, all of which translate into competitive pricing and more affordable service offerings. Furthermore, the seamless integration of RoCEv2 with existing Ethernet networks allows clients to adopt high-performance AI infrastructure without significant reconfiguration or capital investment. This makes RoCEv2 an attractive option for businesses looking to upgrade their AI capabilities without incurring substantial upfront costs.

Moreover, RoCEv2's enhanced reliability and flexibility are critical factors in environments where performance consistency is non-negotiable. Features such as Quality of Service (QoS), priority-based flow control, and congestion management ensure that performance remains predictable and reliable even under heavy loads or in multi-tenant environments. For clients, this means better service quality, fewer disruptions, and more confidence in their data center's ability to handle complex and demanding AI workloads.

The ability to support modern AI frameworks and distributed computing libraries further underscores the versatility of RoCEv2. Compatibility with popular AI frameworks like TensorFlow, PyTorch, and MXNet allows clients to leverage RoCEv2's advantages without needing to overhaul their existing development environments or workflows. This seamless integration ensures that AI teams can continue using their preferred tools while benefiting from enhanced data transfer speeds and reduced latency, fostering innovation and agility.

Conclusion

Overall, AI-driven data centers leveraging RoCEv2 are better positioned to meet the growing demands for high-performance, scalable, and cost-efficient AI solutions. By providing faster, more efficient, and secure data transfer capabilities, RoCEv2 enables companies to deliver superior AI services that can drive digital transformation across industries. Clients, in turn, benefit from more reliable, cost-effective, and high-performing AI infrastructure, which empowers them to innovate rapidly, respond to market changes more effectively, and achieve their business objectives with greater confidence.

Adopting RoCEv2 for testing AI workloads not only enhances the efficiency and performance of data center operations but also provides a differentiated value proposition for companies and their clients. It creates a robust, scalable, and flexible environment capable of supporting cutting-edge AI applications and workloads, ultimately driving growth, competitiveness, and success in the AI-powered digital era. The convergence of AI and RoCEv2 marks a significant step forward in the evolution of modern data centers, setting new standards for how AI workloads are tested, deployed, and managed efficiently and effectively.