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 discusses the launch of the Ascend modeling and simulation platform, which aims to optimize the interaction between load, optimization strategies, and system architecture to enhance infrastructure performance [1]. Group 1: Challenges in AI Model Training - Over 60% of computing power is wasted due to hardware resource mismatches and system coupling, highlighting the inefficiencies in traditional optimization methods [2]. - The training process for large models is likened to "slamming the gas pedal," where the MoE model requires precise balancing of computation and memory to avoid efficiency drops [4]. - Dynamic real-time inference systems face challenges in meeting both high throughput and low latency requirements across varying task types [4]. Group 2: Solutions and Innovations - The "digital wind tunnel" allows for pre-simulation of complex AI models in a virtual environment, enabling the identification of bottlenecks and optimization strategies before real-world implementation [6]. - The Sim2Train framework enhances the efficiency of large-scale training clusters through automatic optimization of deployment space and dynamic performance awareness, achieving a 41% improvement in resource utilization [7]. - The Sim2Infer framework focuses on real-time optimization of inference systems, resulting in over 30% performance improvement through adaptive mixed-precision inference and global load balancing [8]. Group 3: High Availability and Reliability - The Sim2Availability framework ensures high availability of the Ascend computing system, achieving a 98% uptime and rapid recovery from failures through advanced optimization techniques [11]. - The system employs a comprehensive monitoring approach to track hardware states and optimize software fault management, enhancing overall system reliability [13]. Group 4: Future Outlook - As new applications evolve, the demand for innovative system architectures will increase, necessitating continuous advancements in modeling and simulation methods to support the development of computing infrastructure [16].