Optimize your use of hardware-based compute accelerators

Utilizing hardware-based compute accelerators can significantly reduce the energy consumption and carbon footprint of your workloads. These accelerators, like GPUs and TPUs, are designed to perform complex computations more efficiently compared to traditional CPUs, leading to lower physical infrastructure demands and enhanced sustainability outcomes.

Best Practices

Leverage Accelerated Computing Instances

• Evaluate workload requirements to determine if accelerated computing instances (such as GPU and FPGA) can provide necessary performance enhancements while optimizing resource utilization.
• Utilize instance types that are designed for specific tasks to enhance energy efficiency, reducing idle time and overall hardware demand.
• Incorporate cost and performance metrics into decision-making to ensure that the benefits of utilizing accelerated instances outweigh their costs.
• Regularly review and adjust instance usage based on evolving workload needs to avoid over-provisioning and ensure that you’re leveraging the most efficient services.
• Implement automation tools to dynamically allocate and deallocate accelerated resources based on demand, minimizing the environmental impact during low activity periods.

Optimize Workload Distribution

• Distribute workloads intelligently across available compute resources to prevent bottlenecks that may necessitate additional hardware.
• Analyze and balance workloads to maximize throughput while minimizing the need for excess hardware, leading to a lower carbon footprint.
• Use serverless computing opportunities where applicable to eliminate the need for dedicated instances, further reducing hardware dependency.

Monitor and Adjust Resource Utilization

• Implement monitoring solutions to track the performance and utilization of accelerated instances, allowing for data-driven adjustments.
• Conduct regular audits of hardware usage to identify underutilized resources, reallocating or phasing out inefficient hardware as needed.
• Train teams to understand the sustainability implications of their architectural choices, promoting a culture of efficiency within the organization.

Questions to ask your team

• Have you assessed the compute requirements of your workloads to determine the need for accelerated computing instances?
• What criteria do you use to select specific hardware accelerators for your workloads?
• Are you regularly monitoring the utilization of your compute accelerators to ensure they are being used efficiently?
• How do you plan to scale your use of hardware-based compute accelerators as demand changes?
• Have you considered switching to serverless architectures or managed services that may already incorporate optimized hardware features?

Who should be doing this?

Cloud Architect

• Evaluate and select appropriate accelerated computing instances for workloads.
• Design architectures that leverage compute accelerators to optimize resource usage.
• Ensure alignment between hardware choices and sustainability goals.

DevOps Engineer

• Implement CI/CD pipelines that utilize optimized accelerated computing resources.
• Monitor workload performance and adjust resource allocation to maximize efficiency.
• Collaborate with the Cloud Architect to identify opportunities for further optimization.

Sustainability Officer

• Define sustainability goals and metrics for cloud usage.
• Provide guidance on best practices for sustainable hardware usage.
• Measure and report on sustainability impact of cloud resources and recommend improvements.

Financial Analyst

• Analyze cost implications of using accelerated computing instances.
• Provide insights on budget adjustments to focus on sustainable choices.
• Evaluate the financial benefits of reducing physical infrastructure through optimization.

What evidence shows this is happening in your organization?

• Sustainability Hardware Optimization Checklist: A comprehensive checklist to evaluate the current use of hardware in your architecture, focusing on identifying opportunities to implement hardware-based compute accelerators effectively to minimize environmental impact.
• Accelerated Computing Adoption Strategy: A strategic plan outlining the steps to integrate accelerated computing instances into existing workloads, detailing potential benefits, implementation processes, and impact assessments on sustainability goals.
• Sustainability Dashboard Template: A customizable dashboard template for tracking and visualizing the efficiency of hardware utilization, including metrics on the impact of accelerated computing in reducing physical infrastructure requirements.
• Accelerated Instances Utilization Report: A sample report that provides insights into the usage of accelerated computing instances, their performance, and how they align with the organization’s sustainability objectives.
• Best Practices Guide for Hardware Optimization: A guide that outlines best practices for optimizing hardware usage, with a focus on leveraging compute accelerators to enhance sustainability outcomes.

Cloud Services

AWS

• Amazon EC2 P4 Instances: These are optimized for machine learning and high-performance computing which reduces the amount of physical hardware needed.
• AWS Lambda: Serverless compute that automatically scales and can minimize infrastructure usage based on demand.
• AWS Graviton2: Instances powered by Graviton2 processors provide better price/performance and energy efficiency for workloads.

Azure

• Azure N-Series Virtual Machines: These VMs leverage GPU capabilities for compute-intensive tasks, enhancing performance while minimizing resource usage.
• Azure Functions: A serverless compute option that allows you to run code without provisioning or managing servers, optimizing resource allocation.
• Azure Batch: This service enables large-scale parallel and high-performance computing efficiently, optimizing resource utilization.

Google Cloud Platform

• Google Cloud Compute Engine with NVIDIA GPUs: Offers powerful GPU instances for accelerating machine learning and graphics-intensive workloads, reducing hardware footprint.
• Google Cloud Functions: A serverless execution environment for building and connecting cloud services that reduces infrastructure consumption.
• Google Kubernetes Engine (GKE): Managed Kubernetes service that allows efficient resource management and scaling of workloads.

Question: How do you select and use cloud hardware and services in your architecture to support your sustainability goals?
Pillar: Sustainability (Code: SUS)