Introduction to Distributed File Systems for AI
Understand why large-scale AI training demands parallel file systems and how distributed storage architectures keep GPU clusters fed with data.
The Storage Challenge in AI Training
Modern AI training jobs run across hundreds or thousands of GPUs simultaneously. Each GPU needs to read training data at rates that saturate its processing capacity. A single NVMe drive or network-attached storage volume simply cannot deliver the aggregate throughput required when thousands of processes read data concurrently.
What Makes a File System “Distributed”?
A distributed file system spreads data across multiple storage servers and presents a unified namespace to clients. Key architectural components include:
-
Metadata Servers (MDS)
Track file locations, permissions, and directory structure. Separated from data to enable parallel data access without metadata bottlenecks.
-
Object/Data Storage Servers (OSS/OST)
Store actual file data striped across multiple disks and servers. More servers means more aggregate bandwidth.
-
Client Software
Runs on compute nodes and presents the distributed storage as a standard POSIX mount point. Applications read and write files without knowing data is distributed.
-
High-Speed Network
InfiniBand or high-bandwidth Ethernet connects clients to storage servers. Network bandwidth often determines storage performance limits.
Distributed File Systems vs Object Storage
| Feature | Distributed File System | Object Storage |
|---|---|---|
| Interface | POSIX (mount point) | REST API |
| Latency | Sub-millisecond | 10-100ms |
| Throughput | TB/s aggregate | GB/s per prefix |
| Random I/O | Excellent | Poor |
| Cost | Higher (dedicated hardware) | Lower (shared infrastructure) |
| Best For | Active training, checkpoints | Long-term data, artifacts |
AI I/O Patterns
Understanding how AI workloads access storage is essential for choosing and tuning a distributed file system:
Training Data Reads
Sequential reads of large files or many small files. High bandwidth, many concurrent readers. Data is typically read multiple times across epochs.
Checkpoint Writes
Periodic large sequential writes from all processes simultaneously. Creates burst write patterns that can overwhelm storage if not properly configured.
Logging and Metrics
Many small append writes from every training process. High metadata overhead. Often benefits from a separate lightweight file system.