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Answer: Use Amazon FSx for Lustre as a shared file system. Link the file system to an Amazon S3 bucket for postprocessing.
## Explanation **Correct Answer: C - Use Amazon FSx for Lustre as a shared file system. Link the file system to an Amazon S3 bucket for postprocessing.** **Why this is correct:** 1. **Low latency requirement (1 ms):** Amazon FSx for Lustre is specifically designed for high-performance computing workloads and provides sub-millisecond latency, which meets the 1 ms requirement. 2. **Parallel access to shared file system:** FSx for Lustre is a parallel file system that supports concurrent access from hundreds of EC2 instances simultaneously, which is essential for HPC workloads. 3. **Integration with Amazon S3:** FSx for Lustre can be linked to an S3 bucket, allowing: - Data to be automatically copied from S3 to the Lustre file system for high-performance processing - Processed data to be written back to S3 for long-term storage and postprocessing - Engineers to access the final dataset from S3 for manual postprocessing 4. **HPC optimization:** Lustre is specifically designed for HPC workloads with features like: - High throughput for large datasets - Parallel file access patterns - Optimized for compute-intensive workloads **Why other options are incorrect:** **A. Amazon EFS:** While EFS provides a shared file system, it typically has higher latency (single-digit milliseconds) and is optimized for general-purpose workloads, not HPC workloads requiring sub-millisecond latency. **B. Mounting S3 bucket:** S3 cannot be mounted as a traditional file system with POSIX semantics. While there are tools like S3FS, they don't provide the low latency (1 ms) required and aren't suitable for parallel processing from hundreds of instances. **D. AWS Resource Access Manager (RAM):** This service is for sharing AWS resources across accounts, not for creating a high-performance shared file system. S3 buckets shared via RAM still have the same limitations as option B. **Key AWS Services:** - **Amazon FSx for Lustre:** High-performance parallel file system for HPC - **Amazon S3:** Object storage for long-term data storage and postprocessing - **Amazon EC2:** Compute instances for running the HPC workload
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A solutions architect needs to host a high performance computing (HPC) workload in the AWS Cloud. The workload will run on hundreds of Amazon EC2 instances and will require parallel access to a shared file system to enable distributed processing of large datasets. Datasets will be accessed across multiple instances simultaneously. The workload requires access latency within 1 ms. After processing has completed, engineers will need access to the dataset for manual postprocessing.
Which solution will meet these requirements?
A
Use Amazon Elastic File System (Amazon EFS) as a shared file system. Access the dataset from Amazon EFS.
B
Mount an Amazon S3 bucket to serve as the shared file system. Perform postprocessing directly from the S3 bucket.
C
Use Amazon FSx for Lustre as a shared file system. Link the file system to an Amazon S3 bucket for postprocessing.
D
Configure AWS Resource Access Manager to share an Amazon S3 bucket so that it can be mounted to all instances for processing and postprocessing.