LSSUS01-BP01 Design high-performance computing workloads to minimize energy usage
Design research computing workloads to optimize resource utilization and minimize energy consumption through strategic workload separation and queue management. Implement compute environments that align with specific computational demands to maximize hardware efficiency and reduce idle time. Use multi-queue strategies that match workload characteristics to appropriate compute resources, enabling concurrent processing of diverse research tasks while minimizing environmental impact.
Desired outcome: Achieve optimal resource utilization across research computing infrastructure while reducing energy consumption and operational costs. Implement workload-specific compute environments that maximize hardware efficiency for different types of life sciences analyses.
Common anti-patterns:
-
You run computational workloads on the same compute environment regardless of resource requirements.
-
You don't implement queue management strategies to optimize resource allocation.
-
You use oversized compute instances for lightweight computational tasks.
-
You don't use spot instances for fault-tolerant research workloads.
-
You run compute resources continuously without considering actual utilization patterns.
-
You don't separate CPU-intensive, GPU-intensive, and memory-intensive workloads.
Benefits of establishing this best practice:
-
Reduce energy consumption and carbon footprint of research computing operations.
-
Lower operational costs through optimized resource utilization and spot instance usage.
-
Enable concurrent processing of diverse research workloads without resource conflicts.
-
Achieve better cost predictability through workload-specific resource allocation.
-
Support regulatory requirements for sustainable research practices.
Level of risk exposed if this best practice is not established: High
Implementation guidance
Research computing workloads in life sciences vary significantly in their resource requirements, from CPU-intensive sequence alignment to GPU-accelerated molecular dynamics simulations. Implementing workload-specific compute environments verifies that each type of analysis runs on optimally configured resources, reducing both energy consumption and costs. This approach is particularly important for life sciences organizations that must balance research productivity with sustainability goals while maintaining adherence to regulatory requirements.
Consider the computational characteristics of your research workloads when designing compute environments. Genomics pipelines typically require high CPU and memory resources, while protein structure prediction benefits from GPU acceleration. By separating these workloads into dedicated environments, you can achieve better resource utilization and reduce energy waste from oversized or underutilized compute instances.
Implementation steps
-
Analyze and categorize your research computing workloads by resource requirements:
-
Profile CPU, memory, and GPU utilization patterns for different analysis types.
-
Use AWS Compute Optimizer to identify optimal instance types for each workload category.
-
Consider AWS Batch for orchestrating containerized research workloads.
-
-
Design compute environments aligned with workload characteristics:
-
Create CPU-optimized environments for sequence alignment and variant calling.
-
Configure GPU-optimized environments for molecular dynamics and deep learning.
-
Set up memory-optimized environments for genome assembly and phylogenetic analysis.
-
Consider AWS ParallelCluster for HPC workload management.
-
-
Implement multi-queue strategy for efficient resource allocation:
-
Configure high-priority queues for time-sensitive analyses using on-demand instances.
-
Set up spot instance queues for fault-tolerant workloads like Monte Carlo simulations.
-
Use Amazon EC2 Spot Fleet for cost-effective processing of batch workloads.
-
Implement AWS Batch job queues with different compute environments.
-
-
Enable automated scaling and resource optimization:
-
Configure auto scaling policies based on queue depth and resource utilization.
-
Use AWS Auto Scaling to automatically adjust compute capacity.
-
Implement scheduled scaling for predictable workload patterns.
-
Monitor resource utilization with Amazon CloudWatch metrics.
-
-
Establish monitoring and optimization processes:
-
Track energy efficiency metrics using normalized compute hours per analysis.
-
Monitor cost and utilization patterns with AWS Cost Explorer.
-
Set up alerts for underutilized resources using Amazon CloudWatch alarms.
-
Regularly review and optimize compute environment configurations.
-
Resources
Related best practices:
Related documents:
Related videos:
Related examples:
Related tools:
