Keysight’s AI Test Platform: Optimizing AI Networks for Data Centers
The Keysight AI Data Center Test Platform is designed to meet the unique demands of high-performance communications within AI clusters. It provides capabilities such as emulating AI workloads, benchmarking AI network infrastructure, and co-tuning AI cluster performance, making it ideal for sustaining complex AI workloads in the data centers.
Why Is Networking Important in AI Data Centers?
The AI data center relies on a robust network infrastructure known as the AI Fabric. This network facilitates the transfer of large volumes of data between AI Hosts and ensures fast, high-capacity communication to effectively train AI models.
A reliable and efficient network infrastructure prevents data bottlenecks that can slow down AI model training and reduce overall infrastructure efficiency. Keysight’s AI Data Center Test Platform provides a comprehensive solution for testing, validating, and optimizing these networks to meet the demanding requirements of modern AI workloads.
Key Features of the AI Data Center Test Platform
Emulate AI Workloads:
The platform emulates real-world AI workloads, allowing AI operators to stress-test their networks with the same data patterns in production environments. This helps identify performance issues and bottlenecks before they affect real-world AI applications.
Benchmark AI Network Infrastructure:
The platform can benchmark key network elements, assessing the AI Fabric's performance. This process is crucial in ensuring that the network infrastructure can effectively handle AI workloads' high-throughput, low-latency demands.
Co-tune AI Cluster Performance:
Optimizing the AI infrastructure and the network fabric is essential to achieving peak performance. The platform offers tools to co-tuneAI hosts and their connected network infrastructure.
Integrated Software, Hardware with a Fabric Test Methodology for AIWorkloads
Keysight’s AI Data Center Test Platform delivers flexible and comprehensive solutions through both software and hardware components, tailored to optimize AI workloads and network performance.
The software solution offers cost-effective validation, supports new transport protocols, and is ideal for production and cloud environments. Key benefits include minimal overhead with NIC+Fabric co-tuning for efficient network management. This solution enables AI emulation and workload validation without the need for large-scale infrastructure, making it suitable for cloud-based environments.
The hardware solution focuses on isolated fabric validation, providing a high-performance infrastructure capable of handling up to 800G throughput and delivering deep network insights. It allows users to test and validate their AI infrastructure while emulating real-world AI workloads, ensuring reliable results for performance benchmarking.
At the core of both solutions is the Fabric Test Methodology, which ensures that network fabrics are validated independently and optimized for AI data center demands. This methodology allows users to qualify AI network fabric efficiency in job completion time, performance isolation, load balancing, and congestion control mechanisms.
Conclusion:
Keysight's AI Data Center Test Platform is designed to validate network design at the core. AI clusters depend on efficient communication for optimal performance, and this platform provides superior capabilities for validating and fine-tuning network architectures. It is essential for identifying weak points in the AI fabric or optimizing data flow between GPUs, making it crucial for building next-generation AI data centers.
For more information please refer to our AI Data Center Fabric Test Methodology Black Book.
The Keysight AI Data Center Test Platform is designed to meet the unique demands of high-performance communications within AI clusters. It provides capabilities such as emulating AI workloads, benchmarking AI network infrastructure, and co-tuning AI cluster performance, making it ideal for sustaining complex AI workloads in the data centers.
Why Is Networking Important in AI Data Centers?
The AI data center relies on a robust network infrastructure known as the AI Fabric. This network facilitates the transfer of large volumes of data between AI Hosts and ensures fast, high-capacity communication to effectively train AI models.
A reliable and efficient network infrastructure prevents data bottlenecks that can slow down AI model training and reduce overall infrastructure efficiency. Keysight’s AI Data Center Test Platform provides a comprehensive solution for testing, validating, and optimizing these networks to meet the demanding requirements of modern AI workloads.
Key Features of the AI Data Center Test Platform
Emulate AI Workloads:
The platform emulates real-world AI workloads, allowing AI operators to stress-test their networks with the same data patterns in production environments. This helps identify performance issues and bottlenecks before they affect real-world AI applications.
Benchmark AI Network Infrastructure:
The platform can benchmark key network elements, assessing the AI Fabric's performance. This process is crucial in ensuring that the network infrastructure can effectively handle AI workloads' high-throughput, low-latency demands.
Co-tune AI Cluster Performance:
Optimizing the AI infrastructure and the network fabric is essential to achieving peak performance. The platform offers tools to co-tuneAI hosts and their connected network infrastructure.
Integrated Software, Hardware with a Fabric Test Methodology for AIWorkloads
Keysight’s AI Data Center Test Platform delivers flexible and comprehensive solutions through both software and hardware components, tailored to optimize AI workloads and network performance.
The software solution offers cost-effective validation, supports new transport protocols, and is ideal for production and cloud environments. Key benefits include minimal overhead with NIC+Fabric co-tuning for efficient network management. This solution enables AI emulation and workload validation without the need for large-scale infrastructure, making it suitable for cloud-based environments.
The hardware solution focuses on isolated fabric validation, providing a high-performance infrastructure capable of handling up to 800G throughput and delivering deep network insights. It allows users to test and validate their AI infrastructure while emulating real-world AI workloads, ensuring reliable results for performance benchmarking.
At the core of both solutions is the Fabric Test Methodology, which ensures that network fabrics are validated independently and optimized for AI data center demands. This methodology allows users to qualify AI network fabric efficiency in job completion time, performance isolation, load balancing, and congestion control mechanisms.
Conclusion:
Keysight's AI Data Center Test Platform is designed to validate network design at the core. AI clusters depend on efficient communication for optimal performance, and this platform provides superior capabilities for validating and fine-tuning network architectures. It is essential for identifying weak points in the AI fabric or optimizing data flow between GPUs, making it crucial for building next-generation AI data centers.
For more information please refer to our AI Data Center Fabric Test Methodology Black Book.
