Optimizing job completion time and reducing AI training time
Optimizing AI data center fabrics requires a consistent and repeatable testing methodology. This process involves deploying high-density network testbeds, configuring traffic generators to simulate AI data transmission patterns, and using performance analyzers to measure key performance indicators (KPIs) such as job completion time, network throughput, and latency. These tools provide network architects with insights into performance bottlenecks and areas for optimization.
The methodology includes setting up the test beds, configuring the traffic generators to emulate realistic AI workloads, and using performance analyzers to collect and analyze data. By reproducing network communication patterns of real-world AI training jobs, network architects can validate that the network fabric can support the demands of AI training, troubleshoot types of collective operations that demonstrate low performance, and drill down to identify bottlenecks.
AI data center workload emulation solution
The Keysight Data Center Builder Workload Emulation solution software emulates large-scale AI workloads, reducing the need for costly GPU systems in lab setups. Emulating AI data center workloads requires software that can effectively emulate LLM training models for reliable and scalable testing. It leverages high-density traffic generators or affordable servers to emulate AI workload behavior, ensuring alignment with data center infrastructure for optimal performance and efficiency. This solution ensures that the emulated workloads align with data center infrastructure, optimizing performance and efficiency. It also supports high-density AI host emulation, making it possible to mirror AI cluster behavior and validate network fabric performance accurately.
