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This paper develops a multidimensional metric framework to assess sustainability in Multi-Scale High-Performance Computing (HPC) systems, focusing on efficient resource management. By analyzing Architectural Performance metrics alongside Sustainability and Accuracy indicators, the study uncovers intricate trade-offs, particularly between accuracy and energy consumption. The findings provide actionable guidelines for optimizing resource allocation in hybrid architectures, which is crucial for enhancing the performance of demanding applications like AI and Quantum Computing.
Uncovering the trade-offs between accuracy and energy consumption could redefine how we optimize resource allocation in high-performance computing.
The transition from traditional High Performance Computing (HPC) to the Computing Continuum emphasizes efficient resource management and sustainable practices across Multi-Scale hybrid architectures. This paper introduces a multidimensional metric framework to characterize these systems and guide deployment strategies for modern workloads. The framework combines Architectural Performance metrics (such as Throughput, Latency, Scalability), System Utilization, and key Sustainability and Accuracy indicators (such as Energy Efficiency and Power Consumption). Using a modular hybrid testbed, experiments reveal complex relationships among metrics, especially the trade-offs between accuracy and energy, and the efficiency of hybrid nodes. The guidelines help identify optimal operating points and lay the groundwork for improving orchestrators and schedulers (e.g., Kubernetes) to assign demanding applications, including AI and Quantum Computing, to suitable system modules, ensuring high performance and sustainability.
