Choosing the right storage option for HPC workloads

With so many choices available for cloud data storage, it becomes challenging to decide which storage option to pick for HPC workloads. The choice of data storage depends heavily on the use case and performance, throughput, latency, scaling, archival, and retrieval requirements.

For use cases where we need to archive our object data for a very long time, Amazon S3 should be considered. In addition, Amazon S3 can be very well suited to several HPC applications since it can be accessed by other AWS services. For example, in Amazon SageMaker, we can carry out feature engineering using data stored in Amazon S3 and then ingest those features in the SageMaker offline feature store, which is, again, stored in Amazon S3. Amazon SageMaker uses Amazon S3 for ML model training. It reads data from Amazon S3 and carries out model fitting, hyperparameter optimization, and validation using this data. The model artifacts created as a result are...