Storage system latency has a significant impact on large model training, primarily because training processes involve frequent and massive data reads and writes. Large models, especially deep learning models with billions of parameters, require loading vast datasets, checkpointing model weights, and accessing intermediate results repeatedly during training epochs. High storage latency can lead to idle GPU or CPU time while waiting for data, reducing overall training efficiency and increasing time-to-convergence.
Key Impacts:
- GPU/CPU Utilization: Training pipelines are often optimized to keep compute resources fully utilized. If the storage system has high latency, the compute units may sit idle waiting for data to be fetched, leading to underutilization and slower training.
- Training Throughput: Latency affects how quickly batches of data can be prepared and fed into the model. High-latency storage slows down the data pipeline, reducing the number of iterations or batches processed per second.
- Checkpointing Overhead: During long training runs, models are periodically saved (checkpointed) to storage. High latency increases the time taken to save these checkpoints, which can delay recovery in case of interruptions.
- Data Loading Bottlenecks: For large datasets stored on disk (e.g., images, text corpora), slow storage can become a bottleneck, especially if the data isn't optimally pre-fetched or cached.
Example:
Imagine training a large transformer-based language model on a cluster of GPUs. The training process involves reading tokenized text data from storage, preprocessing it, and feeding it into the model in mini-batches. If the underlying storage system (e.g., traditional HDDs or even poorly optimized SSDs) has high read latency, the GPUs will spend a significant amount of time waiting for the next batch of data. This delay can reduce GPU utilization from, say, 90% to 50%, effectively doubling the training time.
Mitigation Strategies:
- Use Low-Latency Storage: Opt for high-performance SSDs (e.g., NVMe) over traditional HDDs.
- Data Caching and Prefetching: Implement caching mechanisms to store frequently accessed data in faster memory (e.g., RAM or local SSDs).
- Distributed File Systems: Use distributed storage systems designed for high throughput and low latency, such as Tencent Cloud's Cloud File Storage (CFS) or High-Performance Cloud Block Storage (CBS) with NVMe support.
- Data Preprocessing and Sharding: Preprocess data in advance and shard it across multiple storage nodes to parallelize access and reduce latency.
- In-Memory Data Loading: Where possible, load and keep datasets in memory (RAM) to avoid repeated storage access.
For workloads requiring high-performance storage, Tencent Cloud's CBS with NVMe SSDs offers ultra-low latency and high IOPS, making it suitable for large-scale model training scenarios. Additionally, Tencent Cloud's CFS provides scalable, shared file storage with consistent performance, ideal for distributed training environments.