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- Why Storage Matters for AI 存储为何对AI至关重要 [2024/03]
Try to summarize the talk "Why Storage Matters for AI"
中文的报道和总结：存储为何对AI至关重要
核心就是：AI是大数据驱动的，特别是最近的大模型，都需要大量的数据去训练。这显然离不开高效的存储系统。另外GPU是高速并行处理Unit，如果存储太慢，就会对GPU形成瓶颈，也就降低了GPU的性能。
- The importance of storage in AI workloads is discussed, emphasizing the need for efficient scaling to meet growing dataset and model complexities.
- Key points covered include the growing AI market, the transition from centralized to distributed computing and storage, and the significance of storage in various AI workflow stages. （centralized to distributed? or the reverse?）
- SSDs offer significant advantages over HDDs in terms of Total Cost of Ownership (TCO), considering factors like power consumption, space, and cooling.
- A case study from Kingsoft Cloud showcases the substantial reduction in data processing time achieved through adopting all-flash arrays.
- The immense future potential of AI and the core role of SSDs in efficient AI computation are highlighted.
- Flash Memory Advantages:
- Flash memory demonstrates significant performance advantages over traditional hard disk drives (HDDs), particularly in terms of I/O parameters.
- The D5-P5430 product shows substantial performance improvement compared to a conventional 24TB HDD.
- Multi-functional Storage Devices:
- Storage devices within a system often serve multiple purposes and operate across various channels simultaneously, contributing to complex mixed I/O workloads.
- SSDs excel in handling concurrent or multi-tenant environments, especially in the face of mixed traffic.
- Total Cost of Ownership (TCO):
- Calculating TCO involves considering numerous complex factors, and comprehensive TCO calculators are essential for accurate assessments.
- Innovative flash storage solutions like the D5-P5336 offer significant cost savings compared to HDDs, particularly in terms of power consumption, footprint reduction, and environmental sustainability.
- Drive Density and Efficiency:
- SSDs offer higher drive densities, leading to space and power efficiency gains, ultimately reducing the number of required servers and racks.
- Comparisons based on per-watt effective disk capacity highlight substantial cost savings due to higher drive capacities.
- GPU Utilization and Performance:
- High-performance storage solutions contribute to maximizing the efficiency of GPU clusters, ensuring continuous high-performance computing during training processes.
- Checkpoint mechanisms play a crucial role in maintaining GPU utilization and minimizing downtime due to storage-related operations.
- AI Workload Processing:
- AI workload processing involves various stages, including data collection, storage, preprocessing, training, and inference, each demanding efficient storage solutions.
- Different types of AI models and workflows require tailored storage solutions to optimize performance and handle diverse workload characteristics effectively.