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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

  • Assess Workload Requirements: Evaluate the computational needs of your workloads to determine the suitability of using accelerators. Proper assessment ensures you choose the right hardware, ultimately optimizing performance and reducing power usage.
  • Choose the Right Instance Types: Select the appropriate instance types that incorporate hardware accelerators and are designed for your specific application requirements. Using optimized instance types can lead to significant reductions in resource consumption.
  • Monitor and Analyze Performance: Implement monitoring tools to analyze the performance and energy usage of the accelerators. Regular insights can help in fine-tuning resource allocation for better energy efficiency.

Supporting Questions

  • Have you evaluated the suitability of hardware accelerators for your workloads?

Roles and Responsibilities

  • Cloud Architect: Responsible for designing cloud architectures that incorporate hardware-based compute accelerators to optimize performance and sustainability.
  • DevOps Engineer: Ensures the deployment and operation of workloads utilizing hardware accelerators are optimized for sustainability.

Artifacts

  • Workload Assessment Report: Document that details the computational requirements of the various workloads to identify opportunities for hardware accelerator utilization.
  • Performance Monitoring Dashboard: A tool that visualizes the performance metrics and energy consumption of various workloads, enabling better resource optimization.

Cloud Services

AWS

  • Amazon EC2 P3 Instances: These instances deliver high-performance GPUs for deep learning tasks, allowing for greater computational efficiency while minimizing energy use.
  • AWS Lambda: Serverless computing that scales automatically based on workload, optimizing resource utilization and reducing the need for excess hardware.

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

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