Core Viewpoint - The performance of storage systems is crucial for enhancing overall training efficiency in AI, as insufficient storage performance can significantly limit GPU utilization [2] Summary by Sections MLPerf Storage v2.0 and Testing Loads - MLPerf Storage is a benchmark suite designed to replicate real AI training loads, assessing storage systems' performance in distributed training environments [3] - The latest version, v2.0, includes three types of training loads that represent the most common I/O patterns in deep learning [3] Specific Training Loads - The 3D U-Net medical segmentation load requires handling large 3D medical images, focusing on throughput performance during sequential reads [4] - The ResNet-50 image classification load emphasizes high-concurrency random reads, demanding high IOPS from storage systems [4] - The CosmoFlow cosmological prediction load tests small file concurrent access and bandwidth scalability, requiring stable metadata handling and low latency [4][5] Performance Comparison Standards - The testing involved various vendors with different storage types, making horizontal comparisons limited; the focus is on shared file systems for more relevant conclusions [6] - Shared file systems are categorized into Ethernet-based systems and InfiniBand (IB) network solutions, each with distinct performance characteristics [7] Test Results Interpretation - For the 3D U-Net load, Ethernet-based storage products like Oracle and JuiceFS excelled, with JuiceFS supporting the most H100 GPUs and achieving a bandwidth utilization of 86.6% [11] - IB network solutions provided high total bandwidth but often exhibited lower bandwidth utilization, typically below 50% [14] - The CosmoFlow load highlighted the challenges of reading numerous small files, with JuiceFS and Oracle leading in GPU support [16][18] - The ResNet-50 load required high IOPS, with JuiceFS supporting the most GPUs and achieving a bandwidth utilization of 72% among Ethernet solutions [21][24] Conclusion - Understanding the type of storage product, including architecture and hardware resources, is essential for evaluating GPU utilization [27] - Ethernet-based storage solutions offer flexibility and cost-effectiveness while providing excellent performance, making them a popular choice for large-scale AI training [27]