Keysight AI Data Center Builder, as a core component of Keysight’s AI solution portfolio, provides critical support for the design, optimization, and deployment of AI infrastructure through high-fidelity workload simulation and system-level co-validation. Its typical application scenarios span four major areas:
1. AI Cluster Architecture Design and Validation
• Network Topology Optimization:
-- Simulate different Scale-up (intra-host GPU interconnect) and Scale-out (inter-rack networking) architectures to analyze the impact of bandwidth and topology on training efficiency.
-- Optimize data transmission paths by tuning network load balancing and congestion control parameters (e.g., RoCEv2/RDMA protocol configurations), reducing tail latency.
Value: Avoid low GPU utilization caused by network design flaws (real-world improvements of over 40% observed).
• Hardware-Algorithm Co-tuning:
-- Integrate LLM (e.g., GPT, Llama) training tasks and support data parallelism (DP), fully sharded data parallelism (FSDP), 3D parallelism, and other strategies to validate their compatibility with GPU/accelerator hardware.
-- Experiment with different model partitioning schemes to optimize data distribution across compute nodes and reduce parallel communication overhead.
2. Performance Bottleneck Identification and Elimination
• Communication Efficiency Analysis:
-- Reproduce collective communication operations in AI training (e.g., All-Reduce) to identify inefficient data transfer behaviors.
-- Analyze network utilization, congestion hotspots, and their impact on job completion time (JCT), providing quantified optimization recommendations.
• End-to-End Latency Optimization: Detect and resolve training disruptions caused by network jitter or protocol conflicts through tail latency monitoring, reducing training failure rates by up to 43%.
3. Next-Generation Technology Research & Compatibility Testing
• High-Speed Interconnect Validation:
-- Support performance simulation of cutting-edge standards such as 1.6T optical/electrical interconnects, PCIe 6.0/CXL 3.0, and 224G SerDes, identifying signal integrity issues early.
-- Enable cross-layer validation from physical to protocol layers in conjunction with hardware platforms (e.g., INPT-1600GE traffic tester).
• Power and Thermal Management Simulation: Integrate power integrity analysis modules (KAI Power) to predict power consumption and cooling requirements under different workloads, optimizing data center PUE metrics.
4. Production Risk Mitigation
• Pre-Deployment System Validation:
-- Simulate real-world AI workloads (e.g., trillion-parameter model training) in the lab to replace large-scale trial runs, shortening validation cycles by 50%.
-- Ensure stability after deploying new algorithms or protocols through parameter fine-tuning (e.g., batch size, gradient synchronization frequency).
• Fault Scenario Simulation: Inject node failures, link outages, and other anomalies to test cluster fault tolerance and recovery strategies, minimizing operational downtime risks.
Product Overview
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