NCP-AII Question 98
Select 3You are designing an AI cluster for a deep learning workload that involves training large-scale models using NVIDIA GPUs. The workload requires high-performance storage for handling datasets exceeding 100TB and rapid access to intermediate results during training. Which storage considerations should you prioritize when establishing the storage requirements for this cluster design?
- A
Using a storage solution with high IOPS and low latency to handle frequent read/write operations.
- B
Selecting a storage system with large capacity but low performance to reduce costs.
- C
Ensuring compatibility with parallel file systems like Lustre or GPFS for distributed data access.
- D
Relying on local disk storage on individual nodes to avoid network dependencies.
- E
Providing a tiered storage solution that balances performance and cost by using both NVMe SSDs and HDDs.
Show answer and explanation
Correct answers: A, C, E
Explanation
Designing storage for an AI cluster requires balancing performance, scalability, and cost. High IOPS and low latency are essential for ensuring that data access does not bottleneck the GPUs. Distributed file systems enable efficient parallel data access across nodes, which is critical for large-scale training. Tiered storage solutions further optimize costs by using high-performance storage for active datasets and lower-cost storage for less critical data. These considerations collectively ensure a well-optimized storage infrastructure for deep learning workloads.
- A. Correct.
High IOPS (Input/Output Operations Per Second) and low latency are critical for deep learning workloads, as they involve frequent data access and updates during training. Prioritizing performance ensures the GPUs are not idle, waiting for data to be loaded.
- B. Incorrect.
While low-cost storage solutions may appear attractive, they often do not meet the performance requirements of AI and deep learning workloads. Large capacity with low performance would create bottlenecks during model training.
- C. Correct.
Distributed file systems like Lustre or GPFS are designed to handle large-scale data in parallel, making them ideal for AI workloads that involve multiple GPUs and nodes accessing shared storage simultaneously.
- D. Incorrect.
Local disk storage on individual nodes can limit scalability and flexibility in a cluster environment. Networked storage solutions are typically preferred for distributed workloads requiring data sharing.
- E. Correct.
A tiered storage solution allows the use of high-speed NVMe SSDs for active training datasets and slower HDDs for archiving or less frequently accessed data. This approach balances performance and cost effectively.