昇腾“数字化风洞”问世：让AI算力配置从经验驱动迈向建模驱动

Core Insights - The article emphasizes the importance of optimizing hardware and software resources in AI model training and inference to avoid significant computational waste, with over 60% of computing power being wasted due to mismatched hardware resources and system coupling [1][2]. Group 1: Challenges in AI Model Training - The article identifies three main challenges in the dynamic load demand of AI model training, highlighting the ongoing hardware-software interplay [2]. - It discusses the "three-way contradiction" in chip characteristics (computing power, bandwidth, and capacity imbalance) that traditional optimization methods struggle to address [1]. Group 2: Solutions for Optimization - The concept of a "digital wind tunnel" is introduced, allowing for virtual environment simulations before actual AI model training, which helps identify bottlenecks and optimize resource allocation [3]. - The Sim2Train framework is presented as an "efficiency engine" for large-scale training clusters, enabling automatic optimization of resource allocation and memory management, achieving a 41% improvement in performance [3]. Group 3: Inference System Optimization - The Sim2Infer framework is described as a "performance accelerator" for dynamic real-time inference systems, addressing the dual demands of high throughput and low latency in various task scenarios [5]. - The article notes that the inference performance can be improved by over 30% through advanced modeling and optimization techniques [7][12]. Group 4: High Availability and Reliability - The Sim2Availability framework is introduced as a "safety airbag" for large-scale training and inference clusters, achieving a 98% availability rate through various optimization techniques [9]. - The article discusses the importance of real-time monitoring and fault management to ensure the reliability of hardware and software systems [13]. Group 5: Future Outlook - The article anticipates continued innovation in system architecture and modeling methods to support the evolving demands of AI applications, with a focus on enhancing the efficiency and stability of Huawei's Ascend clusters [11].