NCP-AII Question 26
Select 2You are managing an AI training workload on a GPU-accelerated cluster. The workload requires frequent read access to large datasets, while minimizing latency and maximizing throughput. Which storage optimization strategies should you implement to ensure the best performance and efficiency?
- A
Use a high-performance parallel file system such as Lustre or GPFS for dataset storage.
- B
Store datasets on a standard HDD array to reduce costs.
- C
Enable data caching on high-speed NVMe drives close to the compute nodes.
- D
Compress the datasets to save storage space, even if it marginally increases read latency.
- E
Use object storage for the datasets as it is designed for scalability and cost-effectiveness.
Show answer and explanation
Correct answers: A, C
Explanation
Optimizing storage for AI workloads involves balancing low latency, high throughput, and cost considerations. High-performance parallel file systems and NVMe caching are ideal for workloads requiring frequent and fast access to large datasets. Other options, like HDD arrays or object storage, may compromise performance, and compression introduces additional latency due to decompression overhead.
- A. Correct.
High-performance parallel file systems like Lustre or GPFS are designed for AI workloads where large datasets are accessed frequently. They provide high throughput and low latency, making them an optimal choice for such scenarios.
- B. Incorrect.
While HDD arrays are cost-effective, they do not provide the necessary performance for AI workloads, which require high data transfer speeds and low latency.
- C. Correct.
Data caching on high-speed NVMe drives close to compute nodes reduces data transfer time and latency, significantly improving performance in AI training workloads.
- D. Incorrect.
While compression can save storage space, it introduces additional compute overhead during decompression, which may negatively impact performance in scenarios requiring frequent data access.
- E. Incorrect.
Object storage is suitable for scalability and cost-effectiveness but is not optimized for low-latency, high-throughput access required for AI training workloads.