Research on MuE_shui Effective Computing Efficiency System Construction, Hardware-Software Decoupling and Global Intelligent Evaluation

Aiming at the structural shortcomings of traditional high-performance computing and artificial intelligence computing evaluation systems, including hardware-oriented distortion, confusion of virtual and real computing power, coupling of hardware and software contributions, poor working condition adaptability, and inability to quantify algorithm value and intelligent capability, this paper originally constructs the MuE_shui effective computing efficiency evaluation system. Through the three-level splitting mechanism of “total computing volume—invalid redundant component—effective contribution component”, a new computing statistical logic centered on effective scientific output is established, breaking the limitations of traditional indicators such as peak FLOPS, hardware utilization, and PUE that only characterize hardware status. This paper further defines the normalized MuE efficiency index and constructs a five-dimensional standardized dimensional system covering core effective computing power, hardware and software contribution rate, global energy efficiency, effective conversion rate, and MuE efficiency. It realizes the accurate decoupling of hardware and software computing contributions, the global unified envelope of efficiency under complex working conditions, and the integrated quantitative evaluation of HPC computing and AI intelligent capability. The experimental results show that traditional E-level supercomputers suffer from cluster redundancy and iterative oscillation, with MuE efficiency of only 0.18–0.32 and severe invalid computing loss; the optimized system in this paper achieves a MuE efficiency of more than 0.90, realizing a qualitative improvement in effective computing utilization. This system completely eliminates computing bubbles, removes the interference of hardware scale and working condition differences on evaluation results, and upgrades computing evaluation from hardware load appearance to scientific output essence. It provides a new standardized theoretical and technical support for high-performance simulation optimization, lightweight computing construction, low-carbon energy efficiency grading, and quantitative evaluation of AI intelligent capability.