軍事用途におけるレジリエンス
実環境のネットワーク条件をエミュレートし、アプリケーションやシステムを検証します
Addressing the network challenges essential for AI efficiency.
Meeting the demands of AI workloads
AI workloads rely on high-performance, low-latency networks to process massive datasets and ensure timely job completion. However, challenges such as congestion, resource utilization, and path inefficiencies can hinder performance. Advanced solutions like Telemetry Assisted Ethernet use real-time data analytics to dynamically optimise network paths, reducing latency and maintaining operational efficiency. Additionally, mitigating network impairments such as jitter, latency, and packet reordering is essential to prevent delays and ensure the scalability and reliability of AI infrastructure.
Ensuring AI performance through High fidelity network emulation.
AI infrastructure testing use cases
Comprehensive testing of clusters and devices to identify opportunities for performance optimization
To improve resource utilisation purchasing additional GPUs to boost performance is often seen as the only option, however adding more compute resource does not speed up job completion time, it can actually increase issues on the network such as congestion and reduce performance.
Optimizing resources to be resilient to network failures and congestion is the best way to ensure AI workload efficiency.
Emulating the real-world high performance network conditions and introducing impairments like delay, jitter, and packet reordering, allows for the comprehensive testing of GPU clusters, highlighting opportunities for performance optimization.
AI fabric twinning for network optimization
Utilize the Calnex SNE-X to create a performance twin of your AI network fabric. Emulate real-world network conditions, including packet delays, reordering, and error injection, to ensure robust and reliable AI model deployment.
The SNE-X enables comprehensive testing and twinning of your AI infrastructure, giving valuable insight into performance and resilience in production environments.
Ensuring positive user experiences with application layer testing
Calnex network emulators enable QA testing of AI-powered application platforms. Simulate diverse network conditions to test and optimise user experience before deployment.
Ensure your application performs reliably under various scenarios, including network delays, packet loss, and jitter. Calnex network emulators, identify potential issues early, guaranteeing positive user experience for end users.
Read our latest blogs on the challenges into optimizing AI infrastructure performance.
Test and measurement products for AI
