NCP-AII Question 29
Select 2You are deploying an AI training workload on a cluster using NVIDIA GPUs. The dataset is large and accessed frequently during training. To optimize storage performance and reduce training time, which of the following strategies should you implement?
- A
Use a high-performance parallel file system like Lustre or GPFS.
- B
Store the dataset on a traditional HDD for cost efficiency.
- C
Enable caching on local NVMe drives to reduce latency.
- D
Compress the dataset to save storage space and minimize I/O overhead.
- E
Store the dataset on a cloud object storage service like Amazon S3.
Show answer and explanation
Correct answers: A, C
Explanation
Optimizing storage for AI training workloads requires balancing performance and efficiency. Using high-performance parallel file systems ensures high throughput and low latency, while caching on local NVMe drives further reduces data access latency. These strategies are critical for improving AI training performance, especially when working with large datasets.
- A. Correct.
Using a high-performance parallel file system like Lustre or GPFS ensures high throughput and low latency for large-scale AI workloads, which is essential for optimizing storage performance during training.
- B. Incorrect.
Traditional HDDs are cost-efficient but have slower read/write speeds, which can bottleneck AI training workloads. This is not an optimal choice for high-performance storage.
- C. Correct.
Enabling caching on local NVMe drives provides fast access to frequently used data, reducing latency and improving performance during AI training.
- D. Incorrect.
While compression can save storage space, it adds computational overhead to decompress data during training, potentially increasing latency and slowing down training performance.
- E. Incorrect.
Storing the dataset on a cloud object storage service like Amazon S3 is suitable for archival or infrequent access but does not provide the necessary performance for high I/O workloads like AI training.