The rise of LLMs and other AI workloads has placed immense pressure on AI data center infrastructure, especially the network. As your AI models grow in size and complexity, they require ever-increasing bandwidth, lower latency, and greater complexity to move data between GPUs during training. This demand has pushed traditional networking solutions to the limits, driving your need for specialized testing tools to ensure optimal performance.

The Inefficiencies and Bottlenecks in AI Networks

Typical network traffic comprises variable packet sizes, bursty transmission patterns, and long-tail distributions of packet arrival times, packet sizes, and flow durations. Optimizing networks to support this traffic means emulating this behavior – either through simulations or through traffic generators.

However, these methodologies are insufficient for evaluating and optimizing the performance of AI networks because real-world AI workloads exhibit unique characteristics due to the intensive communication required for parallel processing across multiple GPUs:

• Flow Dependencies: AI training often involves collective operations (like all-reduce, all-to-all) where data transfers between GPUs occur in a series. The completion of each step depends on the previous one, which makes delays or high latency in the network accumulate and degrade overall performance. These sequential data transfers are tightly coupled — even small amounts of network jitter or latency can impact training times significantly, as downstream processes are delayed.
• Homogeneous and Large Data Packets: AI training workloads frequently involve transferring large data payloads, such as gradients or model parameters, which results in sizable data packets being moved across the network. These workloads rely on a narrow range of communication protocols (e.g., RDMA for low-latency, high-throughput communication) and tend to use similar-sized data packets across the network. This homogeneity can cause load-balancing challenges, especially in highly congested networks.
• Bursty Transmission Patterns: GPUs send data in bursts as they complete training iterations, causing sharp spikes in network traffic. These bursty transmissions can overwhelm network buffers, leading to congestion, packet loss, or retransmissions. AI models, especially when training in distributed settings, require synchronization between GPUs at regular intervals (e.g., gradient updates during backpropagation). These synchronization points produce bursts of traffic, which can create bottlenecks.
• High Bandwidth and Low Latency Requirements: The need to transfer large volumes of data between GPUs, often across data centers, demands significant bandwidth to ensure efficient communication. Latency between GPUs during training can become a critical bottleneck. Even small delays in communication can slow down training cycles, leading to inefficiencies that can substantially extend training times.
• Long-Lived Flows: AI training jobs typically run for extended periods (days, weeks, or even months). This means that the network connections established between GPUs during training tend to remain active for long durations, further straining the network.
• Collective Communication Patterns: These collective operations are fundamental to distributed AI training. They require that all nodes (GPUs) communicate with each other to exchange gradients and model parameters in a coordinated manner. This leads to highly interconnected traffic patterns that must be efficiently managed to avoid congestion. AI training generates a symmetrical pattern of traffic, where each GPU both sends and receives similar amounts of data. This symmetry makes it difficult for load balancers to distribute traffic evenly across the network.
• Congestion Hotspots: In data centers with leaf-spine architectures, the spine switches can experience significant congestion, as the symmetrical and bursty nature of AI traffic challenges load-balancing mechanisms and exhausts available bandwidth. Bottlenecks tend to emerge at certain points in the network, particularly in oversubscribed environments, leading to reduced throughput and increased latency.
• In-Cast Traffic Patterns: AI workloads often generate in-cast traffic patterns, where many GPUs send data to a single destination (e.g., during gradient aggregation). This can overwhelm the receiver’s network interface and cause buffer overflows, leading to packet loss and reduced performance.

The costs of the AI infrastructure and the duration of training jobs – weeks to months – means that you need to keep the infrastructure busy at all times. You don’t want to interrupt AI training to optimize your network, and you want to be able to reconfigure from one AI workload to the next as fast as possible.

These challenges highlight your need for testing tools that can accurately emulate the behavior of your real AI workloads and expose network vulnerabilities that would otherwise remain hidden.

Keysight’s Solution: Emulation and Deep Insights

Keysight Technologies has developed the AI Data Center Test Platform to address these challenges. The platform emulates AI workloads, specifically the network-intensive communication patterns of GPU collectives, enabling comprehensive testing and optimization of AI networks without the need for expensive and scarce GPUs.

Keysight’s AI Data Center Test Platform tackles these challenges with two key approaches:

• Emulated AI Workload: The platform utilizes either real network interface cards (NICs) or specialized hardware traffic generators to generate traffic that mimics the patterns and characteristics of actual AI workloads. This allows for testing networks with realistic traffic profiles, revealing performance bottlenecks and vulnerabilities that would be missed with traditional testing methods.
• Deep Network Insights: Keysight’s platform provides granular visibility into network performance metrics, including flow completion times, latencies, and buffer utilization. Crucially, it achieves nanosecond-level resolution in these measurements, a feat that is both technologically challenging and expensive to achieve. This level of precision is essential for identifying and mitigating subtle performance issues that can significantly impact AI training times.

Addressing AI Infrastructure Challenges

The platform is particularly beneficial if you are:

• Hyperscale Operators: It allows for pre-deployment testing and validation of AI network infrastructure, ensuring that networks can handle the demands of large-scale AI workloads before costly GPUs are deployed.
• Network Equipment Manufacturers: It provides a comprehensive testing environment for evaluating the performance of new networking technologies and features, such as enhanced ECMP hashing and congestion control schemes, under realistic AI traffic conditions.
• ASIC and Accelerator Vendors: It enables testing and optimization of new hardware designs, including NICs and SmartNICs, to ensure compatibility and performance with AI workloads.

Two Key Applications

The platform comes with two pre-packaged applications:

• Collective Benchmark: This application ensures you can measure the performance of specific collective operations, such as all-reduce and all-to-all, by comparing achieved throughput to theoretical limits. It is ideal for you if you have a deep understanding of your AI workloads and the collective operations they utilize.
• Workload Replay: This application replays network traces captured from real AI training jobs, enabling you to benchmark network performance under realistic workload conditions. This is particularly useful if you may not have the expertise to define specific collective benchmarks but want to evaluate network performance under their specific AI workloads.

Limitations and Future Developments

While the platform offers a powerful solution for testing AI networks, some limitations exist:

• Trace-Based Replay: The workload replay application relies on pre-recorded traces, which may not perfectly capture the dynamic nature of AI workloads.
• Black Box Network: Currently, the platform treats the network as a black box, limiting its ability to provide specific configuration recommendations.

Keysight is actively working to address these limitations. Future developments include:

• Enhanced Trace Replay: Incorporating AI to generate more realistic and dynamic traces.
• Network Topology Awareness: Integrating with network management tools to gather information about the network configuration and provide targeted optimization recommendations.

You need a network that can efficiently support your large-scale AI workloads and with Keysight’s AI Data Center Test Platform you can confidently stay ahead of the curve. By simulating real-world AI traffic and delivering detailed performance analytics, this innovative solution enables you to proactively optimize, eliminating bottlenecks and future-proofing your infrastructure. As your AI complexity continues to escalate, investing in comprehensive network testing and optimization is crucial for you to maintain a competitive edge and unlock the full potential of your AI initiatives.

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