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].

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].

Core Viewpoint - Huawei has introduced a "digital wind tunnel" technology that allows for virtual environment simulations before training complex AI models, aiming to reduce over 60% of computational waste caused by hardware resource mismatches and system coupling [1][2]. Group 1: Digital Wind Tunnel - The digital wind tunnel serves as a virtual platform for simulating AI model training and inference processes, enabling early problem detection and configuration optimization [1][3]. - This technology is likened to automotive wind tunnel testing, where it helps in avoiding inefficiencies during the training phase of AI models [2][3]. Group 2: Sim2Train Platform - Huawei's Sim2Train platform simulates the training process to identify optimal hardware configurations and training strategies, enhancing the performance of Ascend devices [5][9]. - The platform employs a modular approach to build complex models and analyze resource consumption, improving the efficiency of large-scale training clusters [7][8]. Group 3: Sim2Infer Platform - The Sim2Infer platform enhances end-to-end inference performance by 30% through multi-level modeling and simulation of inference systems [13]. - It includes features such as load characteristic simulation, hardware architecture analysis, deployment strategy description, and automatic search optimization for model structures and configurations [14]. Group 4: Sim2Availability Framework - The Sim2Availability framework ensures high availability of large models on clusters by simulating various faults and their impacts, thereby improving system reliability [16][17]. - It utilizes a Markov model to monitor the state of the system and analyze recovery strategies for different types of hardware failures [18][20].