NCP-AII Question 27
Select 3You are managing an AI infrastructure that processes large datasets for training deep learning models. The storage system is experiencing performance bottlenecks during data read and write operations, impacting the training speed. Which of the following actions would help optimize the storage performance for the AI workload?
- A
Implement a tiered storage architecture with faster NVMe drives for frequently accessed data.
- B
Compress all datasets to reduce storage size, even if it increases data access latency.
- C
Ensure the storage system supports parallel I/O operations to maximize throughput.
- D
Move all training data to a cloud-based object storage system for scalability.
- E
Leverage a distributed file system optimized for AI workloads, such as NVIDIA GPFS or Lustre.
Show answer and explanation
Correct answers: A, C, E
Explanation
Optimizing storage performance for AI workloads requires addressing both latency and throughput bottlenecks. Using tiered storage with NVMe drives improves access speed for critical data, while parallel I/O operations maximize the system's ability to handle high data demands. Additionally, distributed file systems optimized for AI workloads ensure efficient data management and accessibility. These strategies collectively enhance storage performance and ensure smooth operation of AI training pipelines.
- A. Correct.
Implementing a tiered storage architecture with faster NVMe drives can significantly reduce data access latency for frequently used data, improving the overall storage performance.
- B. Incorrect.
Compressing datasets can save storage space but often increases data access latency, which would worsen the performance bottlenecks during training.
- C. Correct.
Supporting parallel I/O operations allows the storage system to handle multiple read/write requests simultaneously, increasing throughput and efficiency for AI workloads.
- D. Incorrect.
While cloud-based object storage systems can provide scalability, they often have higher latency compared to on-premises solutions optimized for AI training.
- E. Correct.
Distributed file systems like NVIDIA GPFS or Lustre are designed for high-performance computing and AI workloads, enabling efficient data access and management across multiple nodes.