LSPERF01-BP01 Design and benchmark computing architecture for genomic workloads to optimize cost-performance ratio
Focus on designing architectures that can dynamically scale to accommodate the highly variable workload patterns inherent in genomic sequencing and molecular modeling. Implement auto scaling capabilities, containerized workflows, and serverless processing pipelines that can efficiently handle both massive batch processing jobs and intermittent analysis requests without over-provisioning resources.
Desired outcome: A scalable, cost-efficient, and cloud-based architectural framework that provides optimal performance at every stage of genomic data processing and satisfies high-performance and variable computing needs.
Benefits of establishing this best practice:
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Control costs by matching resource consumption to actual demand.
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Accelerate time-to-insight by removing processing queues and bottlenecks.
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Enable researchers to run analyses without capacity planning.
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Support the growing volume of genomic data generated by next-generation sequencing technologies.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Design your architecture to dynamically scale with the variable workload patterns common in genomic sequencing and molecular modeling. These workloads often exhibit unpredictable patterns, with intense computational demands during certain phases followed by periods of minimal activity.
Configure Amazon EC2 Auto Scaling groups with appropriate scaling policies based on CPU utilization, memory usage, or custom metrics specific to your genomic workloads. Implement predictive scaling when possible, especially for recurring workload patterns in research cycles. Consider using Spot Instances with a fallback mechanism to optimize costs during large-scale genomic data processing.
Package genomic analysis tools and dependencies in containers using Amazon ECS or Amazon EKS to provide consistent runtime environments. Design containers to be stateless, storing intermediate results in Amazon S3 or other durable storage. Use Amazon ECR to manage container images with version control for reproducible research.
Build event-driven architectures using AWS Lambda for sequence alignment, variant calling, and other discrete processing steps. Use AWS Step Functions to orchestrate complex genomic workflows, handling retries and error conditions automatically. Implement AWS Batch for compute-intensive tasks that exceed Lambda's execution limits.
Position compute resources close to data stores to minimize transfer times of large genomic datasets. Implement data partitioning strategies that align with your processing patterns. Consider using AWS HealthOmics for specialized genomic workloads, which provides purpose-built infrastructure that automatically provisions and scales the underlying infrastructure.
Implementation steps
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Deploy containerized genomic workflows on Amazon ECS with Fargate.
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Implement AWS Auto Scaling for variable sequencing workloads.
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Use AWS Batch for cost-efficient molecular modeling jobs.
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Create AWS Step Functions for serverless processing pipelines.
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Use Amazon SageMaker AI for on-demand ML inference scaling.
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Use AWS HealthOmics for storing genomics data and sequence stores.
Resources
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