华为“数字化风洞”小时级预演万卡集群方案，昇腾助力大模型运行“又快又稳”

Core Viewpoint - The article emphasizes the importance of optimizing hardware and software integration in AI model training and inference systems to avoid inefficiencies and maximize computational power [1][2][3]. Group 1: Challenges and Solutions - The article identifies three main challenges in dynamic load demands and the hardware-software interplay, proposing a "digital wind tunnel" for pre-simulation of AI models to identify bottlenecks and optimize resource allocation [2][3]. - The "Sim2Train" framework is introduced as an efficiency engine for large-scale training clusters, addressing issues like resource allocation and communication efficiency to maintain high performance during training [3][4]. Group 2: Performance Optimization Techniques - The "Sim2Infer" framework is presented as a performance accelerator for inference systems, utilizing dynamic optimization techniques to enhance end-to-end inference performance by over 30% [5][10]. - The article discusses a multi-level inference system modeling simulation that integrates various core functions to achieve optimal hardware utilization and low latency in AI applications [10][11]. Group 3: Reliability and Availability - The "Sim2Availability" framework is described as a safety net for large-scale training clusters, ensuring high availability and quick recovery from hardware failures, achieving a 98% availability rate [9][11]. - The article highlights the importance of real-time monitoring and fault management in maintaining the reliability of AI computing systems [9][11]. Group 4: Future Outlook - The article concludes with a vision for continuous innovation in system architecture to support evolving AI applications, emphasizing the need for advanced modeling and simulation techniques to enhance computational infrastructure [12].