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This research introduces a multidimensional framework that evaluates high-performance computing systems by integrating architectural performance with sustainability metrics like energy consumption and computational accuracy. By moving beyond simple peak FLOPS benchmarks, this approach enables intelligent resource allocation for demanding workloads in artificial intelligence and quantum computing across heterogeneous hardware environments. The resulting tools allow orchestration platforms to optimize efficiency and reduce the environmental footprint of complex, multi-scale computing infrastructures.
Researchers Measure Links Between Computing Power & Sustainability

Measuring sustainability in multi-scale computing has long relied on simple benchmarks that fail to capture real-world complexity. This work establishes a framework combining architectural performance with indicators like energy consumption and computational accuracy, a shift enabling better resource allocation. It lays the foundation for tools such as Kubernetes to intelligently deploy power-hungry workloads across diverse systems while optimising efficiency.
A new method characterises complex computing systems spanning multiple scales, enabling better allocation of resources for demanding applications such as artificial intelligence and quantum computing. The multidimensional metric framework assesses architectural performance alongside key indicators of sustainability and accuracy, considering factors like energy efficiency and power consumption during operation. This approach examines how efficiently resources are utilised within complex setups incorporating varied technologies like CPUs and GPUs. Consequently, tools such as Kubernetes…
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