Traditional computer system load optimization strategies have exposed many shortcomings in dealing with continuously changing workloads, power management, and heat dissipation challenges. This article proposes a new computer system load optimization method based on principles of physics. By real-time monitoring and load prediction, combined with principles of thermodynamics and fluid mechanics, load distribution is optimized to ensure efficient allocation of computing resources while preventing overheating. A collaborative utilization method for heterogeneous computing resources has been proposed to balance power consumption and computing resources, fully tapping into the unique performance of various resources to enhance overall efficiency. Through temperature related load distribution strategies, flexible adaptation between heat dissipation and load has been successfully achieved, thereby reducing energy consumption and improving system reliability. This has opened up innovative ideas and means for improving the efficiency of computer systems, reducing energy consumption, and suppressing temperature rise.

Huijie Pan. Discussion on Low-Latency Computing Strategies in Real-Time Hardware Generation. International Journal of Neural Network (2025), Vol. 4, Issue 1: 57-64. https://doi.org/10.38007/NN.2025.040107.

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